fix(telemetry): resolve microsecond latencies below 100us

The microsecond ladder's first edge was 100us, which sat ABOVE the mass of
every instrument using it. Measured on devnet: 99.3% of job_queued_us
samples, 92.5% of job_running_us and 90.4% of getobject_lookup_us fell in
that first bucket. histogram_quantile then interpolated inside bucket 0 and
returned `quantile / fraction_in_bucket_0 x first_edge` -- p75/p95/p99 of
job_queued_us read 75.52/95.66/99.69us against a prediction of
75.53/95.67/99.70. Three-decimal agreement: those panels were reporting
arithmetic on the bucket edge, not latency.

The fix was already half-written. kSubMillisecondBoundaries had been parked
in MetricsRegistry.cpp as [[maybe_unused]] with a comment noting exactly this
problem for nodestore reads. Its edges are now folded into kMicrosecondBuckets
rather than deleted, so the parked intent is carried forward: 1..1000us
resolution where the mass is, upper edges unchanged so multi-second stalls
stay measurable.

Also moves the GetObject count and charge ladders into HistogramBuckets.h, so
all five ladders have one owner and one set of invariant tests (29 now).

Adds check_bucket_parity.py, wired into the existing OTel naming workflow.
The C++ millisecond ladder and the collector's spanmetrics ladder are
specified to agree over their shared range; they were identical when shipped,
then the collector side alone was extended and nothing noticed for eleven
phases. The check asserts containment rather than equality, because jobs
outlive spans -- jobq_updatepaths averages ~60s, which no span approaches, so
demanding equality would force a ceiling that censors it. Verified it rejects
a missing collector edge, a bogus in-range edge, and a return to the 5s
ceiling.

ledger-data-sync's "Job Queue Wait p95 By Type" moves off the beast
jobq_*_q_milliseconds pair onto job_queued_us filtered by job_type. Those
beast metrics are ms-quantised at the source (Event rounds up to a whole
millisecond), so 94-100% of their samples sat in the first bucket and no
ladder change could fix them. Note the label values are camelCase
(job_type="ledgerData"), not the lowercase metric-name fragments.

Both histogram-fed alert thresholds re-validated and left unchanged, with the
measured basis recorded so neither gets tuned against the old artefact: only
0.0022% of job_queued_us samples exceed the 1s threshold, and every edge
bracketing the 1000ms ios_latency threshold survived the ladder change.

Docs: the rpc_size "known issue -- tracked separately" notes in the runbook
and 09-data-collection-reference are now resolved notes, the stale 10-edge
span_duration bucket list is corrected to the collector's real 20, and the
runbook gains a "Reading A Histogram Percentile" section covering both
saturation traps and the expected discontinuity after a ladder change.

include/xrpl/telemetry/HistogramBuckets.h

@@ -136,6 +136,68 @@ inline constexpr std::array kByteBuckets{
     262'144.0,
     1'048'576.0};
 
+/**
+ * Bucket edges, in microseconds, for the OTel-native duration instruments
+ * created directly on MetricsRegistry: job queue wait and run times, RPC
+ * method latency, and GetObject lookup latency.
+ *
+ * The edges from 1 to 1000 us are the ones that matter most. An earlier
+ * version of this ladder started at 100 us, which sat ABOVE the mass of every
+ * instrument using it: 99.3% of job_queued_us samples, 92.5% of
+ * job_running_us and 90.4% of getobject_lookup_us fell in that first bucket.
+ * `histogram_quantile` then interpolated inside bucket 0 and returned the
+ * boundary scaled by the requested quantile -- p75/p95/p99 of job_queued_us
+ * read 75.5/95.7/99.7 us, which is arithmetic on the bucket edge, not a
+ * latency. A warm nodestore read is around 1.5 us, so single-microsecond
+ * resolution is not excessive here.
+ *
+ * The upper edges reach a minute so multi-second stalls stay measurable. The
+ * SDK's own default ladder stops at 10,000, which every one of these
+ * instruments exceeds during catch-up.
+ */
+inline constexpr std::array kMicrosecondBuckets{
+    1.0,
+    2.0,
+    5.0,
+    10.0,
+    25.0,
+    50.0,
+    100.0,
+    250.0,
+    500.0,
+    1'000.0,
+    5'000.0,
+    25'000.0,
+    100'000.0,
+    500'000.0,
+    1'000'000.0,
+    5'000'000.0,
+    10'000'000.0,
+    30'000'000.0,
+    60'000'000.0};
+
+/**
+ * Bucket edges for the GetObject request object count.
+ *
+ * Counts run from 1 to the hard reply cap (kHardMaxReplyNodes, 12288). The
+ * honest sync path asks for at most 8 objects, so the low edges are
+ * fine-grained; the upper ones follow the charge size bands up to the cap.
+ * Because the top edge IS the hard cap, this ladder cannot saturate.
+ */
+inline constexpr std::array
+    kObjectCountBuckets{1.0, 2.0, 4.0, 8.0, 16.0, 64.0, 256.0, 1'024.0, 4'096.0, 12'288.0};
+
+/**
+ * Bucket edges for the GetObject resource charge.
+ *
+ * Charges span 0 (the free tier) to roughly 99k for a full-size all-miss
+ * request. The edges bracket the two thresholds that decide a peer's fate --
+ * the warning threshold at 5000 and the drop threshold at 25000 -- so a
+ * dashboard can show how close charges run to each.
+ */
+inline constexpr std::array
+    kChargeBuckets{0.0, 100.0, 500.0, 1'000.0, 5'000.0, 10'000.0, 25'000.0, 50'000.0, 100'000.0};
+
 /**
  * @brief Check that a ladder is strictly ascending and non-negative.
  *
@@ -163,6 +225,9 @@ isAscendingNonNegative(std::span<double const> ladder) noexcept
 
 static_assert(isAscendingNonNegative(kMillisecondBuckets));
 static_assert(isAscendingNonNegative(kByteBuckets));
+static_assert(isAscendingNonNegative(kMicrosecondBuckets));
+static_assert(isAscendingNonNegative(kObjectCountBuckets));
+static_assert(isAscendingNonNegative(kChargeBuckets));
 
 /**
  * @brief Copy a ladder into the `std::vector<double>` the OTel SDK wants.