Cache initializer silently succeeds on Kubernetes API failures

### What happened?

The cache dataset initializer logs Kubernetes API failures and returns normally in multiple paths:

https://github.com/kubeflow/trainer/blob/7c08264b93d3b83de45d1152b169858cadf1d8b3/pkg/initializers/dataset/cache.py#L104-L135

https://github.com/kubeflow/trainer/blob/7c08264b93d3b83de45d1152b169858cadf1d8b3/pkg/initializers/dataset/cache.py#L335-L344

This lets the initializer process exit successfully even though the cache cluster was not created. It also makes retry cleanup unsafe: when the cache ServiceAccount already exists and LeaderWorkerSet creation returns 409, the outer error handler deletes the pre-existing ServiceAccount and then returns success.

I reproduced both behaviors with mocked Kubernetes clients:

```text
TrainJob GET 500 -> download_dataset returned None
pre-existing ServiceAccount + LeaderWorkerSet 409 -> returned None; ServiceAccount delete called once
```

### What did you expect to happen?

Non-recoverable Kubernetes API errors should propagate so the initializer container fails visibly. AlreadyExists responses should be handled idempotently, and cleanup should only delete resources created by the current invocation.

A focused fix would track resource ownership during creation, handle 409 consistently for the ServiceAccount, LeaderWorkerSet, and Service, perform best-effort cleanup only for newly created resources, and re-raise the original error. Unit tests can cover each failure and retry path.

### Environment

Kubeflow Trainer version: current master at `7c08264b93d3b83de45d1152b169858cadf1d8b3`

Kubernetes version: N/A (reproduced with Kubernetes Python client mocks)

Kubeflow Python SDK version: N/A

### Contribution

I'd like to work on this and submit a focused PR with regression tests once maintainers confirm the desired idempotency behavior.

_AI assistance disclosure: Codex assisted with repository inspection and technical drafting; I reproduced and verified the reported behavior locally._

Start in pkg/initializers/dataset/cache.py at the referenced error paths and inspect the initializer's Kubernetes resource creation and cleanup flow. Reproduce the mocked API failure and retry cases, then add focused unit tests showing that non-recoverable errors propagate, AlreadyExists is idempotent, and cleanup removes only resources created by the current invocation.

Written by the indexing model from the issue text.