### 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)

sagemaker_operation = self.hook.update_endpoint
            response = sagemaker_operation(
                endpoint_info,
                wait_for_completion=False,
            )
        if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
            raise AirflowException(f"Sagemaker endpoint creation failed: {response}")
        if self.deferrable:
            self.defer(
                trigger=SageMakerTrigger(
                    job_name=endpoint_info["EndpointName"],
                    job_type="endpoint",
                    poke_interval=self.check_interval,
                    aws_conn_id=self.aws_conn_id,
                ),
                method_name="execute_complete",
                timeout=datetime.timedelta(seconds=self.max_ingestion_time),
            )
        elif self.wait_for_completion:
            self.hook.get_waiter("endpoint_in_service").wait(
                EndpointName=endpoint_info["EndpointName"],
                WaiterConfig={"Delay": self.check_interval, "MaxAttempts": self.max_ingestion_time},
            )
        return {
            "EndpointConfig": serialize(
                self.hook.describe_endpoint_config(endpoint_info["EndpointConfigName"])
            ),
            "Endpoint": serialize(self.hook.describe_endpoint(endpoint_info["EndpointName"])),
        }
    def execute_complete(self, context, event=None):