### Apache Airflow version

main (development)

### What happened

The provider operator for Google Cloud Dataproc Batches has two bugs: 

1. The running [operator](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/dataproc.py#L2123-L2124) returns successful even if the job transitions to State.CANCELLED or State.CANCELLING 
2. It [attempts](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/dataproc.py#L2154) to 'reattach' to a potentially running job if it AlreadyExists, but it sends the wrong type since 'result' is a Batch and needs Operation

### What you think should happen instead

A new hook that polls for batch job completion.  There is precedent for it in traditional dataproc with 'wait_for_job'.

### How to reproduce

Use the Breeze environment and a DAG that runs DataprocCreateBatchOperator.  Allow the first instance to start. 

Use the gcloud CLI to cancel the job. 

`gcloud dataproc batches cancel <batch_id> --project <project_id> --region <region>`

Observe that the task completes successfully after a 3-5 minute timeout, even though the job was cancelled. 

Run the task again with the same batch_id.  Observe the ValueError where it expects Operation but receives Batch



### Operating System

Darwin 5806 21.6.0 Darwin Kernel Version 21.6.0: Mon Aug 22 20:17:10 PDT 2022; root:xnu-8020.140.49~2/RELEASE_X86_64 x86_64

### Versions of Apache Airflow Providers

Same as dev (main) version. 

### Deployment

Other Docker-based deployment

### Deployment details

Observable in the Breeze environment, when running against real Google Infrastructure. 

### Anything else

Every time. 

### 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)

dag_id=TEST_DAG_ID,
        execution_date=DEFAULT_DATE,
        task_id=TASK_ID,
        cluster_name=CLUSTER_NAME,
        project_id=GCP_PROJECT,
        region=GCP_REGION,
        num_workers=3,
        num_preemptible_workers=2,
        graceful_decommission_timeout="2m",
        gcp_conn_id=GCP_CONN_ID,
    )
    serialized_dag = dag_maker.get_serialized_data()
    deserialized_dag = SerializedDAG.from_dict(serialized_dag)
    deserialized_task = deserialized_dag.task_dict[TASK_ID]
    
    assert serialized_dag["dag"]["tasks"][0]["_operator_extra_links"] == [
        {"airflow.providers.google.cloud.links.dataproc.DataprocLink": {}}
    ]
    
    assert isinstance(deserialized_task.operator_extra_links[0], DataprocLink)
    
    assert ti.task.get_extra_links(ti, DataprocLink.name) == ""
    
    assert deserialized_task.get_extra_links(ti, DataprocLink.name) == ""
    ti.xcom_push(key="conf", value=DATAPROC_CLUSTER_CONF_EXPECTED)
    
    assert deserialized_task.get_extra_links(ti, DataprocLink.name) == DATAPROC_CLUSTER_LINK_EXPECTED
    
    assert ti.task.get_extra_links(ti, DataprocLink.name) == DATAPROC_CLUSTER_LINK_EXPECTED
class TestDataprocClusterDeleteOperator(unittest.TestCase):