### 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)

self,
        *,
        job_flow_id: str | None = None,
        job_flow_name: str | None = None,
        cluster_states: list[str] | None = None,
        aws_conn_id: str = "aws_default",
        steps: list[dict] | str | None = None,
        wait_for_completion: bool = False,
        waiter_delay: int | None = None,
        waiter_max_attempts: int | None = None,
        execution_role_arn: str | None = None,
        **kwargs,
    ):
        if not exactly_one(job_flow_id is None, job_flow_name is None):
            raise AirflowException("Exactly one of job_flow_id or job_flow_name must be specified.")
        super().__init__(**kwargs)
        cluster_states = cluster_states or []
        steps = steps or []
        self.aws_conn_id = aws_conn_id
        self.job_flow_id = job_flow_id
        self.job_flow_name = job_flow_name
        self.cluster_states = cluster_states
        self.steps = steps
        self.wait_for_completion = wait_for_completion
        self.waiter_delay = waiter_delay
        self.waiter_max_attempts = waiter_max_attempts
        self.execution_role_arn = execution_role_arn
    def execute(self, context: Context) -> list[str]: