**Apache Airflow version**: 2.0.2


**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): NA

**Environment**: MWAA and Locally

- **Cloud provider or hardware configuration**: AWS
- **OS** (e.g. from /etc/os-release): NA
- **Kernel** (e.g. `uname -a`): NA
- **Install tools**: NA
- **Others**: NA

**What happened**:

When calling the SagemakerProcessingOperator sometimes get: "botocore.exceptions.ClientError: An error occurred (ThrottlingException)" due to excessive ListProcessingJobs operations.

**What you expected to happen**:

The job should have started without timing out. I believe one fix would be to use the `NameContains` functionality of boto3 [list_processing_jobs](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.list_processing_jobs) so you don't have to paginate as is occurring [here](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/sagemaker.py#L916). 

**How to reproduce it**:

If you incrementally create Sagemaker Processing jobs you will eventually see the Throttling as the pagination increases.

**Anything else we need to know**:

This looks like it is happening when the account already has a lot of former Sagemaker Processing jobs.
tests/providers/amazon/aws/hooks/test_sagemaker.py
mock_check_training.return_value = True
        mock_describe.side_effect = [
            (LogState.WAIT_IN_PROGRESS, DESCRIBE_TRAINING_INPROGRESS_RETURN, 0),
            (LogState.JOB_COMPLETE, DESCRIBE_TRAINING_STOPPING_RETURN, 0),
            (LogState.COMPLETE, DESCRIBE_TRAINING_COMPLETED_RETURN, 0),
        ]
        mock_session = mock.Mock()
        mock_log_session = mock.Mock()
        attrs = {
            'create_training_job.return_value': test_arn_return,
            'describe_training_job.return_value': DESCRIBE_TRAINING_COMPLETED_RETURN,
        }
        log_attrs = {
            'describe_log_streams.side_effect': LIFECYCLE_LOG_STREAMS,
            'get_log_events.side_effect': STREAM_LOG_EVENTS,
        }
        mock_session.configure_mock(**attrs)
        mock_log_session.configure_mock(**log_attrs)
        mock_client.return_value = mock_session
        mock_log_client.return_value = mock_log_session
        hook = SageMakerHook(aws_conn_id='sagemaker_test_conn_id_1')
        hook.create_training_job(
            create_training_params, wait_for_completion=True, print_log=True, check_interval=1
        )
        assert mock_describe.call_count == 3
        assert mock_session.describe_training_job.call_count == 1