### Description

The Python Google Cloud Dataproc API version has been updated in Airflow to support filtering on the Dataproc Batches API.  The DataprocListBatchesOperator can be updated to make use of this filtering.  

Currently, DataprocListBatchesOperator returns, and populates xcom with every job run in the project.  This almost surely will fail as the return object is large and xcom storage is low, especially with MySQL. Filtering on job prefix and create_time are much more useful capabilities. 

### Use case/motivation

The ability to filter lists of GCP Dataproc Batches jobs.  

### Related issues

None

### Are you willing to submit a PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

self,
        region: str,
        filter_: str,
        project_id: str,
        page_size: int | None = None,
        retry: Retry | _MethodDefault = DEFAULT,
        timeout: float | None = None,
        metadata: Sequence[tuple[str, str]] = (),
    ):
        """List all regions/{region}/clusters in a project.
        :param project_id: Google Cloud project ID that the cluster belongs to.
        :param region: Cloud Dataproc region to handle the request.
        :param filter_: To constrain the clusters to. Case-sensitive.
        :param page_size: The maximum number of resources contained in the
            underlying API response. If page streaming is performed
            per-resource, this parameter does not affect the return value. If
            page streaming is performed per-page, this determines the maximum
            number of resources in a page.
        :param retry: A retry object used to retry requests. If *None*, requests