### Apache Airflow Provider(s)

amazon

### Versions of Apache Airflow Providers

apache-airflow-providers-amazon==7.1.0

### Apache Airflow version

2.2.2

### Operating System

macOS Ventura 13.1

### Deployment

Docker-Compose

### Deployment details

_No response_

### What happened

When calling GlueJobOperator and passing 
`create_job_kwargs={"Command": {"Name": "pythonshell"}}` I need to specify MaxCapacity and based on the code [here](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/glue.py#L127) that's equal to _num_of_dpus_ and that parameter is integer as stated [here](https://github.com/apache/airflow/blob/main/airflow/providers/amazon/aws/hooks/glue.py#L68) 
Because I want to use pythonshell, AWS Glue offers to setup ranges between 0.00625 and 1 and that can't be achieved with integer. 
`When you specify a Python shell job (JobCommand.Name="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.`

I was trying to pass _MaxCapacity_ in `create_job_kwargs={"Command": {"Name": "pythonshell"}, "MaxCapacity": 0.0625}` but it throws the error. 

### What you think should happen instead

I think that parameter _num_of_dpus_ should be type double or MaxCapacity should be allowed to setup as double if pythonshell was selected in Command -> Name.

### How to reproduce

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [X] 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)

"""
        Get state of the Glue job.
        The job state can be running, finished, failed, stopped or timeout.
        .. seealso::
            - :external+boto3:py:meth:`Glue.Client.get_job_run`
        :param job_name: unique job name per AWS account
        :param run_id: The job-run ID of the predecessor job run
        :return: State of the Glue job
        """
        job_run = self.get_conn().get_job_run(JobName=job_name, RunId=run_id, PredecessorsIncluded=True)
        return job_run["JobRun"]["JobRunState"]
    def print_job_logs(
        self,
        job_name: str,
        run_id: str,
        job_failed: bool = False,
        next_token: str | None = None,
    ) -> str | None:
        """Prints the batch of logs to the Airflow task log and returns nextToken."""
        log_client = boto3.client("logs")
        response = {}
        filter_pattern = FAILURE_LOG_FILTER if job_failed else DEFAULT_LOG_FILTER