### 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)

"""
        self.check_training_config(config)
        response = self.get_conn().create_training_job(**config)
        if print_log:
            self.check_training_status_with_log(
                config["TrainingJobName"],
                self.non_terminal_states,
                self.failed_states,
                wait_for_completion,
                check_interval,
                max_ingestion_time,
            )
        elif wait_for_completion:
            describe_response = self.check_status(
                config["TrainingJobName"],
                "TrainingJobStatus",
                self.describe_training_job,
                check_interval,
                max_ingestion_time,
            )
            billable_time = (
                describe_response["TrainingEndTime"] - describe_response["TrainingStartTime"]
            ) * describe_response["ResourceConfig"]["InstanceCount"]
            self.log.info("Billable seconds: %d", int(billable_time.total_seconds()) + 1)
        return response
    def create_tuning_job(
        self,
        config: dict,
        wait_for_completion: bool = True,
        check_interval: int = 30,