feat: structured DEBUG/INFO logging via loguru

- common/logging.py: improve format to timestamp|LEVEL|module:func:line - message
- core/ layer: DEBUG log every subprocess invocation (cmd, rc, byte counts),
  JSON load/dump events, parsed application_id. ERROR log on failures.
- tools/ layer: DEBUG log every public tool entry with key parameters,
  INFO log on business outcomes (saved/submitted/killed/...).
- New tests/unit/test_logging.py: capture loguru output via in-memory sink
  and assert DEBUG + INFO messages are emitted for representative flows.

spark_executor/tools/kill.py

@@ -11,11 +11,12 @@ store = JobStore()
 
 
 def kill_job(job_id: str) -> dict[str, str]:
+    logger.debug(f"kill_job enter job_id={job_id}")
     job = store.get(job_id)
     if job is None:
         raise KeyError(f"Unknown job_id: {job_id}")
     kill_application(job.application_id)
-    logger.info(f"kill_job job_id={job_id} application_id={job.application_id}")
+    logger.info(f"kill_job ok job_id={job_id} application_id={job.application_id}")
     return {
         "job_id": job_id,
         "application_id": job.application_id,