### 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)

template_fields: Sequence[str] = (
        "name",
        "virtual_cluster_id",
        "execution_role_arn",
        "release_label",
        "job_driver",
        "configuration_overrides",
    )
    ui_color = "#f9c915"
    def __init__(
        self,
        *,
        name: str,
        virtual_cluster_id: str,
        execution_role_arn: str,
        release_label: str,
        job_driver: dict,
        configuration_overrides: dict | None = None,
        client_request_token: str | None = None,
        aws_conn_id: str = "aws_default",
        wait_for_completion: bool = True,
        poll_interval: int = 30,
        max_tries: int | None = None,
        tags: dict | None = None,
        max_polling_attempts: int | None = None,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        self.name = name
        self.virtual_cluster_id = virtual_cluster_id