### 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.job_error_states = job_error_states if job_error_states is not None else {"ERROR"}
        self.impersonation_chain = impersonation_chain
        self.hook = DataprocHook(gcp_conn_id=gcp_conn_id, impersonation_chain=impersonation_chain)
        self.project_id = self.hook.project_id if project_id is None else project_id
        self.job_template: DataProcJobBuilder | None = None
        self.job: dict | None = None
        self.dataproc_job_id = None
        self.asynchronous = asynchronous
        self.deferrable = deferrable
        self.polling_interval_seconds = polling_interval_seconds
    def create_job_template(self) -> DataProcJobBuilder:
        """Initialize `self.job_template` with default values."""
        if self.project_id is None:
            raise AirflowException(
                "project id should either be set via project_id "
                "parameter or retrieved from the connection,"
            )
        job_template = DataProcJobBuilder(
            project_id=self.project_id,
            task_id=self.task_id,
            cluster_name=self.cluster_name,
            job_type=self.job_type,
            properties=self.dataproc_properties,
        )
        job_template.set_job_name(self.job_name)
        job_template.add_jar_file_uris(self.dataproc_jars)
        job_template.add_labels(self.labels)
        self.job_template = job_template
        return job_template
    def _generate_job_template(self) -> str: