### Apache Airflow version

main (development)

### What happened

The DataprocCreateBatchOperator (Google provider) handles the case when a batch_id already exists in the Dataproc API by 'reattaching' to a potentially running job.  

Current reattachment logic uses the non-deferrable method even when the operator is in deferrable mode.  

### What you think should happen instead

The operator should reattach in deferrable mode. 

### How to reproduce

Create a DAG with a task of DataprocCreateBatchOperator that is long running.  Make DataprocCreateBatchOperator deferrable in the constructor.  

Restart local Airflow to simulate having to 'reattach' to a running job in Google Cloud Dataproc. 

The operator resumes using the running job but in the code path for the non-derferrable logic.  

### Operating System

macOS 13.4.1 (22F82)

### Versions of Apache Airflow Providers

Current main.

### Deployment

Official Apache Airflow Helm Chart

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit 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 PySpark Job on a Cloud DataProc cluster.
    :param main: [Required] The Hadoop Compatible Filesystem (HCFS) URI of the main
            Python file to use as the driver. Must be a .py file. (templated)
    :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
    :param pyfiles: List of Python files to pass to the PySpark framework.
        Supported file types: .py, .egg, and .zip
    """
    template_fields: Sequence[str] = (
        "main",
        "arguments",
        "job_name",
        "cluster_name",
        "region",
        "dataproc_jars",
        "dataproc_properties",
        "impersonation_chain",
    )
    ui_color = "#0273d4"
    job_type = "pyspark_job"
    @staticmethod
    def _generate_temp_filename(filename):