### 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)

tags=["tags"],
            storage_bucket="storage_bucket",
            init_actions_uris=["init_actions_uris"],
            init_action_timeout="10m",
            metadata={"metadata": "data"},
            custom_image_family="custom_image_family",
            custom_image_project_id="custom_image_project_id",
            autoscaling_policy="autoscaling_policy",
            properties={"properties": "data"},
            optional_components=["optional_components"],
            num_masters=2,
            master_machine_type="master_machine_type",
            master_disk_type="master_disk_type",
            master_disk_size=128,
            worker_machine_type="worker_machine_type",
            worker_disk_type="worker_disk_type",
            worker_disk_size=256,
            num_preemptible_workers=4,
            region="region",
            service_account="service_account",
            service_account_scopes=["service_account_scopes"],
            idle_delete_ttl=60,
            auto_delete_time=datetime(2019, 9, 12),
            auto_delete_ttl=250,
            customer_managed_key="customer_managed_key",
            enable_component_gateway=True,
        )
        cluster = generator.make()
        assert CONFIG_WITH_CUSTOM_IMAGE_FAMILY == cluster
class TestDataprocClusterCreateOperator(DataprocClusterTestBase):