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/logs.py

@@ -11,13 +11,14 @@ store = JobStore()
 
 
 def get_job_logs(job_id: str, tail_chars: int = 5000) -> str:
+    logger.debug(f"get_job_logs enter job_id={job_id} tail_chars={tail_chars}")
     job = store.get(job_id)
     if job is None:
         raise KeyError(f"Unknown job_id: {job_id}")
     full = get_application_logs(job.application_id)
     tailed = full[-tail_chars:] if len(full) > tail_chars else full
     logger.info(
-        f"get_job_logs job_id={job_id} application_id={job.application_id} "
-        f"chars={len(tailed)}"
+        f"get_job_logs ok job_id={job_id} application_id={job.application_id} "
+        f"full_chars={len(full)} returned_chars={len(tailed)}"
     )
     return tailed