### Apache Airflow Provider(s)

amazon

### Versions of Apache Airflow Providers



apache-airflow-providers-amazon | 2.4.0

### Apache Airflow version

2.2.3 (latest released)

### Operating System

Amazon Linux 2

### Deployment

MWAA

### Deployment details

_No response_

### What happened

Sagemaker Processing Operator no longer honors the `action_if_job_exists` param and always fails creation of a new processing job is a job with the name already exists. 

This happens because in a recent change, the function responsible for executing the job no longer honors the increment setting:

Change that breaks the increment: https://github.com/apache/airflow/commit/96dd70348ad7e31cfeae6d21af70671b41551fe9

New code: https://github.com/apache/airflow/blob/6734eb1d09a99dc519e89a59e2086cef09a87098/airflow/providers/amazon/aws/operators/sagemaker.py#L167

  

### What you expected to happen

When Sagemaker Processing operator is called with a job-name that already exists, the job creation should succeed with a name that is incremented by 1.   

### How to reproduce

invoke SageMakerProcessingOperator twice with the same job name while keeping action_if_job_exists as 'increment'.   

### 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)

training_job_name = self.config["TrainingJobName"]
        training_jobs = self.hook.list_training_jobs(name_contains=training_job_name)
        if training_job_name in [tj["TrainingJobName"] for tj in training_jobs]:
            if self.action_if_job_exists == "increment":
                self.log.info("Found existing training job with name '%s'.", training_job_name)
                new_training_job_name = f"{training_job_name}-{(len(training_jobs) + 1)}"
                self.config["TrainingJobName"] = new_training_job_name
                self.log.info("Incremented training job name to '%s'.", new_training_job_name)
            elif self.action_if_job_exists == "fail":
                raise AirflowException(
                    f"A SageMaker training job with name {training_job_name} already exists."
                )
class SageMakerDeleteModelOperator(SageMakerBaseOperator):
    """
    Deletes a SageMaker model.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:SageMakerDeleteModelOperator`
    :param config: The configuration necessary to delete the model.
        For details of the configuration parameter see :py:meth:`SageMaker.Client.delete_model`
    :param aws_conn_id: The AWS connection ID to use.
    """
    def __init__(self, *, config: dict, aws_conn_id: str = DEFAULT_CONN_ID, **kwargs):
        super().__init__(config=config, **kwargs)
        self.aws_conn_id = aws_conn_id
    def execute(self, context: Context) -> Any:
        sagemaker_hook = SageMakerHook(aws_conn_id=self.aws_conn_id)
        sagemaker_hook.delete_model(model_name=self.config["ModelName"])
        self.log.info("Model %s deleted successfully.", self.config["ModelName"])