Add the five Ledgerline application metrics

- ledgerline.transactions.total (counter: kind, status, currency) and
  ledgerline.transaction.duration (histogram, seconds: kind) in gateway,
  the only place that sees the final outcome, failures included. currency
  is client input, so values outside the seeded set are recorded as
  "other".
- ledgerline.fraud.score (histogram, buckets in 0.1 steps to 1.0) in fraud.
- ledgerline.stream.lag (observable gauge: consumer_group) in audit and
  notifier, read from XINFO GROUPS through a separate sync Redis client,
  because callbacks run on the scrape thread; the call is untraced so
  scrapes don't create traces.
- ledgerline.db.pool.in_use (observable up-down counter: pool) in every
  service with a DB engine. The SQLAlchemy instrumentation's own pool
  metric names a pool by DSN, which merges the normal and starved engines.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

services/notifier/app.py

@@ -11,6 +11,7 @@ from opentelemetry.trace import StatusCode
 
 from common.db import engine
 from common.logging_config import configure_logging
+from common.metrics import observe_db_pools, observe_stream_lag
 from common.redis_client import redis_client
 from common.telemetry import setup_telemetry
 
@@ -36,6 +37,8 @@ FastAPIInstrumentor.instrument_app(
     exclude_spans=["receive", "send"],
 )
 SQLAlchemyInstrumentor().instrument(engines=[engine.sync_engine])
+observe_db_pools({"default": engine})
+observe_stream_lag(consumer.GROUP)
 
 
 @app.get("/")