### 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)

changed = secondary_training_status_changed(
            SECONDARY_STATUS_DESCRIPTION_1, SECONDARY_STATUS_DESCRIPTION_2
        )
        assert changed
    def test_secondary_training_status_changed_false(self):
        changed = secondary_training_status_changed(
            SECONDARY_STATUS_DESCRIPTION_1, SECONDARY_STATUS_DESCRIPTION_1
        )
        assert not changed
    def test_secondary_training_status_message_status_changed(self):
        now = datetime.now(tzlocal())
        SECONDARY_STATUS_DESCRIPTION_1["LastModifiedTime"] = now
        expected_time = datetime.utcfromtimestamp(time.mktime(now.timetuple())).strftime("%Y-%m-%d %H:%M:%S")
        expected = f"{expected_time} {status} - {message}"
        assert (
            secondary_training_status_message(SECONDARY_STATUS_DESCRIPTION_1, SECONDARY_STATUS_DESCRIPTION_2)
            == expected
        )
    @mock.patch.object(AwsLogsHook, "get_conn")
    @mock.patch.object(SageMakerHook, "get_conn")
    @mock.patch.object(time, "monotonic")
    def test_describe_training_job_with_logs_in_progress(self, mock_time, mock_client, mock_log_client):