**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
},
    'TransformOutput': {
        'S3OutputPath': output_url,
    },
    'TransformResources': {'InstanceType': 'ml.m4.xlarge', 'InstanceCount': 123},
}
create_model_params = {
    'ModelName': model_name,
    'PrimaryContainer': {
        'Image': image,
        'ModelDataUrl': output_url,
    },
    'ExecutionRoleArn': role,
}
create_endpoint_config_params = {
    'EndpointConfigName': config_name,
    'ProductionVariants': [
        {
            'VariantName': 'AllTraffic',
            'ModelName': model_name,
            'InitialInstanceCount': 1,
            'InstanceType': 'ml.c4.xlarge',
        }
    ],
}
create_endpoint_params = {'EndpointName': endpoint_name, 'EndpointConfigName': config_name}
update_endpoint_params = create_endpoint_params
DESCRIBE_TRAINING_COMPLETED_RETURN = {
    'TrainingJobStatus': 'Completed',
    'ResourceConfig': {'InstanceCount': 1, 'InstanceType': 'ml.c4.xlarge', 'VolumeSizeInGB': 10},