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/status.py

@@ -12,9 +12,10 @@ store = JobStore()
 
 
 def get_job_status(job_id: str) -> JobStatus:
+    logger.debug(f"get_job_status enter job_id={job_id}")
     job = store.get(job_id)
     if job is None:
         raise KeyError(f"Unknown job_id: {job_id}")
     state, raw = get_application_status(job.application_id)
-    logger.info(f"get_job_status job_id={job_id} state={state}")
+    logger.info(f"get_job_status ok job_id={job_id} application_id={job.application_id} state={state}")
     return JobStatus(application_id=job.application_id, state=state, raw=raw)