[Observability] Log interrupted processing tasks on unexpected worker death

When a worker drops off the cluster unexpectedly (e.g., due to an OOM kill), the scheduler tracks the processing_keys but previously did not log them to the console. This change surfaces exactly which tasks were interrupted, significantly improving debugging provenance for cluster hangs and memory crashes.

Author: prince8273

distributed/scheduler.py

@@ -5714,6 +5714,12 @@ async def remove_worker(
                 f"Removing worker {ws.address!r} caused the cluster to lose scattered "
                 f"data, which can't be recovered: {lost_keys} ({stimulus_id=})"
             )
+        if not expected and processing_keys:
+            logger.warning(
+                f"Worker {ws.address!r} dropped unexpectedly. "
+                f"Interrupting {len(processing_keys)} processing tasks: "
+                f"{processing_keys} ({stimulus_id=})"
+            )
 
         event_msg = {
             "action": "remove-worker",