Add job queue latency histogram metric (#21260)

## Summary
- Records the time each job spends waiting in the queue (enqueue →
processing start) as an OpenTelemetry histogram
- Metric is broken down by `queue` and `job_name` attributes, enabling
per-queue p50/p95/p99 latency analysis
- Uses BullMQ's native `job.timestamp` for accurate measurement

## Test plan
- [x] Deploy to staging and verify `job/latency-ms` metric appears in
ClickHouse `otel_metrics_histogram` table
- [x] Confirm Grafana dashboard can query the histogram data

packages/twenty-server/src/engine/core-modules/message-queue/drivers/bullmq.driver.ts

@@ -114,6 +114,15 @@ export class BullMQDriver
         Sentry.withIsolationScope(async () => {
           applyWorkspaceSentryContextFromJobData(job.data);
 
+          const queueLatency = Math.max(0, Date.now() - job.timestamp);
+
+          this.metricsService.recordHistogram({
+            key: MetricsKeys.JobLatencyMs,
+            value: queueLatency,
+            unit: 'ms',
+            attributes: { queue: queueName, job_name: job.name },
+          });
+
           // TODO: Correctly support for job.id
           const timeStart = performance.now();
           const workspaceId = job.data?.workspaceId;

packages/twenty-server/src/engine/core-modules/metrics/types/metrics-keys.type.ts

@@ -40,6 +40,7 @@ export enum MetricsKeys {
   JobCompleted = 'job/completed',
   JobFailed = 'job/failed',
   JobWaiting = 'job/waiting',
+  JobLatencyMs = 'job/latency-ms',
   AiChatTurnLatencyMs = 'ai-chat/turn-latency-ms',
   AiChatStepLatencyMs = 'ai-chat/step-latency-ms',
   AiChatTtftMs = 'ai-chat/ttft-ms',