### 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)

ExecutionEngine=execution_engine,
            ServiceRole=self.service_role,
            NotebookInstanceSecurityGroupId=self.notebook_instance_security_group_id,
            Tags=self.tags,
        )
        if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
            raise AirflowException(f"Starting notebook execution failed: {response}")
        self.log.info("Notebook execution started: %s", response["NotebookExecutionId"])
        notebook_execution_id = response["NotebookExecutionId"]
        if self.wait_for_completion:
            emr_hook.get_waiter("notebook_running").wait(
                NotebookExecutionId=notebook_execution_id,
                WaiterConfig=prune_dict(
                    {
                        "Delay": self.waiter_delay,
                        "MaxAttempts": self.waiter_max_attempts,
                    }
                ),
            )
            
            
            
            failure_states = {"FAILED"}
            final_status = emr_hook.conn.describe_notebook_execution(
                NotebookExecutionId=notebook_execution_id
            )["NotebookExecution"]["Status"]
            if final_status in failure_states:
                raise AirflowException(f"Notebook Execution reached failure state {final_status}.")
        return notebook_execution_id
class EmrStopNotebookExecutionOperator(BaseOperator):