### 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)

last_describe_job_call = time.monotonic()
        last_description = description
        while True:
            time.sleep(check_interval)
            sec += check_interval
            state, last_description, last_describe_job_call = self.describe_training_job_with_log(
                job_name,
                positions,
                stream_names,
                instance_count,
                state,
                last_description,
                last_describe_job_call,
            )
            if state == LogState.COMPLETE:
                break
            if max_ingestion_time and sec > max_ingestion_time:
                
                raise AirflowException(f"SageMaker job took more than {max_ingestion_time} seconds")
        if wait_for_completion:
            status = last_description["TrainingJobStatus"]
            if status in failed_states:
                reason = last_description.get("FailureReason", "(No reason provided)")
                raise AirflowException(f"Error training {job_name}: {status} Reason: {reason}")
            billable_time = (
                last_description["TrainingEndTime"] - last_description["TrainingStartTime"]
            ) * instance_count
            self.log.info("Billable seconds: %d", int(billable_time.total_seconds()) + 1)
    def list_training_jobs(