LocalProcessBackend.list_jobs() returns TrainJob objects with Unknown status instead of actual job status

[kingabdulsalamsas111-lgtm]

What happened?

While looking through the LocalProcessBackend implementation, I noticed that list_jobs() and get_job() don't return consistent TrainJob objects.

get_job() calculates the job status using __get_job_status() before creating the TrainJob, but list_jobs() creates the same object without setting the status field.

Since TrainJob.status defaults to Unknown, every job returned by list_jobs() reports Unknown even if the underlying job has already completed, failed, or is running.

What did you expect to happen?

I expected list_jobs() to return the same status information as get_job(), since both APIs represent the same TrainJob.

How to reproduce

Looking at the implementation:

get_job() does:

status = self.__get_job_status(_job)

return types.TrainJob(
...
status=status,
)

But `list_jobs()` constructs the object without setting `status`:


types.TrainJob(
    name=_job.name,
    creation_timestamp=_job.created,
    runtime=runtime,
    num_nodes=1,
    steps=[...],
)


I also reproduced this with a small unit test by creating a local job whose step status is `TRAINJOB_COMPLETE`. Calling `list_jobs()` still returns a `TrainJob` with `status == "Unknown"` instead of `"Complete"`.

 **Why this matters**

This makes the behavior inconsistent between `list_jobs()` and `get_job()`. A caller using `list_jobs()` cannot rely on the returned status, even though the backend already has enough information to determine it.



### What did you expect to happen?

I expected list_jobs() to return the actual status of each TrainJob, consistent with get_job(). If a job has already completed, failed, or is running, list_jobs() should report that status instead of always returning Unknown.

### Environment

Kubernetes version:
```bash
$ kubectl version

Kubeflow Trainer version:

$ kubectl get pods -n kubeflow -l app.kubernetes.io/name=trainer -o jsonpath="{.items[*].spec.containers[*].image}"

Kubeflow Python SDK version:

$ pip show kubeflow

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