### 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)

project_id = self.custom_image_project_id or self.project_id
            custom_image_url = (
                "https://www.googleapis.com/compute/beta/projects/"
                f"{project_id}/global/images/family/{self.custom_image_family}"
            )
            cluster_data["master_config"]["image_uri"] = custom_image_url
            if not self.single_node:
                cluster_data["worker_config"]["image_uri"] = custom_image_url
        cluster_data = self._build_gce_cluster_config(cluster_data)
        if self.single_node:
            self.properties["dataproc:dataproc.allow.zero.workers"] = "true"
        if self.properties:
            cluster_data["software_config"]["properties"] = self.properties
        if self.optional_components:
            cluster_data["software_config"]["optional_components"] = self.optional_components
        cluster_data = self._build_lifecycle_config(cluster_data)
        if self.init_actions_uris:
            init_actions_dict = [
                {"executable_file": uri, "execution_timeout": self._get_init_action_timeout()}
                for uri in self.init_actions_uris
            ]
            cluster_data["initialization_actions"] = init_actions_dict
        if self.customer_managed_key:
            cluster_data["encryption_config"] = {"gce_pd_kms_key_name": self.customer_managed_key}
        if self.autoscaling_policy:
            cluster_data["autoscaling_config"] = {"policy_uri": self.autoscaling_policy}
        if self.enable_component_gateway:
            cluster_data["endpoint_config"] = {"enable_http_port_access": self.enable_component_gateway}
        return cluster_data
    def make(self):