### Apache Airflow version

2.6.0

### What happened

When marking an EMR Serverless job as failed, the job continues to run.

### What you think should happen instead

The job should be cancelled. Looking at the [EMR Serverless Operator](https://github.com/apache/airflow/blob/a6be96d92828a86e982b53646a9e2eeca00a5463/airflow/providers/amazon/aws/operators/emr.py#L939), I don't see an `on_kill` method, so assuming we just need to add that. 

I'm not sure how to handle the `EmrServerlessCreateApplicationOperator` operator, though - if the workflow has a corresponding `EmrServerlessDeleteApplicationOperator`, we'd probably want to delete the application if the job is cancelled.

### How to reproduce

- Start an EMR Serverless DAG
- Mark the job as failed in the Airflow UI
- See that the EMR Serverless job continues to run

### Operating System

n/a

### Versions of Apache Airflow Providers

`apache-airflow-providers-amazon==8.0.0`

### Deployment

Amazon (AWS) MWAA

### Deployment details

_No response_

### Anything else

_No response_

### 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)

emr_hook = EmrHook(aws_conn_id=self.aws_conn_id)
        emr = emr_hook.get_conn()
        if self.do_xcom_push:
            context["ti"].xcom_push(key="cluster_id", value=self.cluster_id)
        EmrClusterLink.persist(
            context=context,
            operator=self,
            region_name=emr_hook.conn_region_name,
            aws_partition=emr_hook.conn_partition,
            job_flow_id=self.cluster_id,
        )
        EmrLogsLink.persist(
            context=context,
            operator=self,
            region_name=emr_hook.conn_region_name,
            aws_partition=emr_hook.conn_partition,
            job_flow_id=self.cluster_id,
            log_uri=get_log_uri(emr_client=emr_hook.conn, job_flow_id=self.cluster_id),
        )
        self.log.info("Modifying cluster %s", self.cluster_id)
        response = emr.modify_cluster(
            ClusterId=self.cluster_id, StepConcurrencyLevel=self.step_concurrency_level
        )
        if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
            raise AirflowException(f"Modify cluster failed: {response}")
        else:
            self.log.info("Steps concurrency level %d", response["StepConcurrencyLevel"])
            return response["StepConcurrencyLevel"]
class EmrTerminateJobFlowOperator(BaseOperator):