### Description

Today, Amazon provider package supports SageMaker operators for a few operations, like training, tuning, pipelines, but it lacks the support for SageMaker Notebook instances. Boto3 provides necessary APIs to [create notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_notebook_instance.html), [start notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/start_notebook_instance.html), [stop network instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/stop_notebook_instance.html) and [delete notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/delete_notebook_instance.html). Leveraging these APIs, we should add new operators to SageMaker set under Amazon provider. At the same time, having a sensor (synchronous as well as deferrable) for notebook instance execution that utilizes [describe notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/describe_notebook_instance.html) and waits for Stopped/Failed status would help with observability of the execution.

### Use case/motivation

Data Scientists are orchestrating ML use cases via Apache Airflow. A key component of ML use cases is running Jupyter Notebook on SageMaker. Having built-in operators and sensors would make it easy for Airflow users to run Notebook instances on SageMaker.

### Related issues

_No response_

### Are you willing to submit a PR?

- [ ] 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)

self.preprocess_config()
        model_info = self.config.get("Model")
        endpoint_config_info = self.config.get("EndpointConfig")
        endpoint_info = self.config.get("Endpoint", self.config)
        if model_info:
            self.log.info("Creating SageMaker model %s.", model_info["ModelName"])
            self.hook.create_model(model_info)
        if endpoint_config_info:
            self.log.info("Creating endpoint config %s.", endpoint_config_info["EndpointConfigName"])
            self.hook.create_endpoint_config(endpoint_config_info)
        if self.operation == "create":
            sagemaker_operation = self.hook.create_endpoint
            log_str = "Creating"
        elif self.operation == "update":
            sagemaker_operation = self.hook.update_endpoint
            log_str = "Updating"
        else:
            raise ValueError('Invalid value! Argument operation has to be one of "create" and "update"')
        self.log.info("%s SageMaker endpoint %s.", log_str, endpoint_info["EndpointName"])
        try:
            response = sagemaker_operation(
                endpoint_info,
                wait_for_completion=False,
            )
            
        except ClientError:
            self.operation = "update"