### 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)

impersonation_chain=self.impersonation_chain,
                    polling_interval_seconds=self.polling_interval_seconds,
                ),
                method_name="execute_complete",
            )
        elif not self.asynchronous:
            self.log.info("Waiting for job %s to complete", new_job_id)
            self.hook.wait_for_job(
                job_id=new_job_id, region=self.region, project_id=self.project_id, timeout=self.wait_timeout
            )
            self.log.info("Job %s completed successfully.", new_job_id)
        return self.job_id
    def execute_complete(self, context, event=None) -> None:
        """
        Callback for when the trigger fires - returns immediately.
        Relies on trigger to throw an exception, otherwise it assumes execution was
        successful.
        """
        job_state = event["job_state"]
        job_id = event["job_id"]
        if job_state == JobStatus.State.ERROR:
            raise AirflowException(f"Job failed:\n{job_id}")
        if job_state == JobStatus.State.CANCELLED:
            raise AirflowException(f"Job was cancelled:\n{job_id}")
        self.log.info("%s completed successfully.", self.task_id)
        return job_id
    def on_kill(self):
        if self.job_id and self.cancel_on_kill:
            self.hook.cancel_job(job_id=self.job_id, project_id=self.project_id, region=self.region)
class DataprocUpdateClusterOperator(BaseOperator):