### 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)

"""Start a Spark Job on a Cloud DataProc cluster.
    :param main_jar: The HCFS URI of the jar file that contains the main class
        (use this or the main_class, not both together).
    :param main_class: Name of the job class. (use this or the main_jar, not both
        together).
    :param arguments: Arguments for the job. (templated)
    :param archives: List of archived files that will be unpacked in the work
        directory. Should be stored in Cloud Storage.
    :param files: List of files to be copied to the working directory
    """
    template_fields: Sequence[str] = (
        "arguments",
        "job_name",
        "cluster_name",
        "region",
        "dataproc_jars",
        "dataproc_properties",
        "impersonation_chain",
    )
    ui_color = "#0273d4"
    job_type = "spark_job"
    def __init__(