**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
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 = '{} {} - {}'.format(
            datetime.utcfromtimestamp(time.mktime(now.timetuple())).strftime('%Y-%m-%d %H:%M:%S'),
            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):