### 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)

self.cluster_config = cluster_config
        self.cluster_name = cluster_name
        self.labels = labels
        self.project_id = project_id
        self.region = region
        self.request_id = request_id
        self.retry = retry
        self.timeout = timeout
        self.metadata = metadata
        self.gcp_conn_id = gcp_conn_id
        self.delete_on_error = delete_on_error
        self.use_if_exists = use_if_exists
        self.impersonation_chain = impersonation_chain
        self.virtual_cluster_config = virtual_cluster_config
        self.deferrable = deferrable
        self.polling_interval_seconds = polling_interval_seconds
    def _create_cluster(self, hook: DataprocHook):
        return hook.create_cluster(
            project_id=self.project_id,
            region=self.region,
            cluster_name=self.cluster_name,
            labels=self.labels,
            cluster_config=self.cluster_config,
            virtual_cluster_config=self.virtual_cluster_config,
            request_id=self.request_id,
            retry=self.retry,
            timeout=self.timeout,
            metadata=self.metadata,
        )
    def _delete_cluster(self, hook):