Is there a way to sync the journal of data service with checkpoint?
### Issue type

Support

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

source

### TensorFlow version

tf 2.6

### Custom code

No

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

In my app, I set fault_tolerant_mode True with data service, and save_checkpoint_steps is 5000 in tf.train.MonitoredTrainingSession.
For example, the app is killed when the global step is 9500, and then I resume the app with checkpoint from the step 5000, but the data service will continue consume the data from the step 9500. It skip about 4500 steps data.

Is there a way to sync the journal of data service with checkpoint, or make the skip less?

### Standalone code to reproduce the issue

```shell
# dispatcher server snippet
dispatcher_server = tf.data.experimental.service.DispatchServer(
    tf.data.experimental.service.DispatcherConfig(
        fault_tolerant_mode=True,
        work_dir=...
    ))

# train worker
dataset = dataset.apply(tf.data.experimental.service.distribute(service=dispatcher_service,...)

with tf.train.MonitoredTrainingSession(checkpoint_dir=checkpoint_dir,
                                       save_checkpoint_steps=5000, ...) as mon_sess:
```


### Relevant log output

_No response_