### 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)

**kwargs,
    ) -> None:
        
        if cluster_config is None and virtual_cluster_config is None:
            warnings.warn(
                f"Passing cluster parameters by keywords to `{type(self).__name__}` will be deprecated. "
                "Please provide cluster_config object using `cluster_config` parameter. "
                "You can use `airflow.dataproc.ClusterGenerator.generate_cluster` "
                "method to obtain cluster object.",
                AirflowProviderDeprecationWarning,
                stacklevel=1,
            )
            
            if "params" in kwargs:
                del kwargs["params"]
            
            if project_id is None:
                raise AirflowException(
                    "project_id argument is required when building cluster from keywords parameters"
                )
            kwargs["project_id"] = project_id
            cluster_config = ClusterGenerator(**kwargs).make()
            
            cluster_params = inspect.signature(ClusterGenerator.__init__).parameters
            for arg in cluster_params:
                if arg in kwargs:
                    del kwargs[arg]
        super().__init__(**kwargs)
        if deferrable and polling_interval_seconds <= 0:
            raise ValueError("Invalid value for polling_interval_seconds. Expected value greater than 0")