**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
'TrainingStartTime': datetime(2018, 2, 17, 7, 15, 0, 103000),
    'TrainingEndTime': datetime(2018, 2, 17, 7, 19, 34, 953000),
    'ResponseMetadata': {
        'HTTPStatusCode': 200,
    },
}
DESCRIBE_TRAINING_INPROGRESS_RETURN = dict(DESCRIBE_TRAINING_COMPLETED_RETURN)
DESCRIBE_TRAINING_INPROGRESS_RETURN.update({'TrainingJobStatus': 'InProgress'})
DESCRIBE_TRAINING_FAILED_RETURN = dict(DESCRIBE_TRAINING_COMPLETED_RETURN)
DESCRIBE_TRAINING_FAILED_RETURN.update({'TrainingJobStatus': 'Failed', 'FailureReason': 'Unknown'})
DESCRIBE_TRAINING_STOPPING_RETURN = dict(DESCRIBE_TRAINING_COMPLETED_RETURN)
DESCRIBE_TRAINING_STOPPING_RETURN.update({'TrainingJobStatus': 'Stopping'})
message = 'message'
status = 'status'
SECONDARY_STATUS_DESCRIPTION_1 = {
    'SecondaryStatusTransitions': [{'StatusMessage': message, 'Status': status}]
}
SECONDARY_STATUS_DESCRIPTION_2 = {
    'SecondaryStatusTransitions': [{'StatusMessage': 'different message', 'Status': status}]
}
DEFAULT_LOG_STREAMS = {'logStreams': [{'logStreamName': job_name + '/xxxxxxxxx'}]}
LIFECYCLE_LOG_STREAMS = [
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
    DEFAULT_LOG_STREAMS,
]
DEFAULT_LOG_EVENTS = [