### 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)

stacklevel=2,
                )
            self.action_if_job_exists = action_if_job_exists
        else:
            raise AirflowException(
                f"Argument action_if_job_exists accepts only 'timestamp', 'increment' and 'fail'. \
                Provided value: '{action_if_job_exists}'."
            )
        self.deferrable = deferrable
        self.serialized_model: dict
        self.serialized_tranform: dict
    def _create_integer_fields(self) -> None:
        """Set fields which should be cast to integers."""
        self.integer_fields: list[list[str]] = [
            ["Transform", "TransformResources", "InstanceCount"],
            ["Transform", "MaxConcurrentTransforms"],
            ["Transform", "MaxPayloadInMB"],
        ]
        if "Transform" not in self.config:
            for field in self.integer_fields:
                field.pop(0)
    def expand_role(self) -> None:
        """Expand an IAM role name into an ARN."""
        if "Model" not in self.config:
            return
        config = self.config["Model"]
        if "ExecutionRoleArn" in config:
            hook = AwsBaseHook(self.aws_conn_id, client_type="iam")
            config["ExecutionRoleArn"] = hook.expand_role(config["ExecutionRoleArn"])
    def execute(self, context: Context) -> dict: