### Apache Airflow version

2.3.0

### What happened

When using a `PythonVirtualenvOperator` it is checked if `virtualenv` is installed by 
`if not shutil.which("virtualenv"):`
https://github.com/apache/airflow/blob/a1679be85aa49c0d6a7ba2c31acb519a5bcdf594/airflow/operators/python.py#L398
Actually, this expression checks if `virtualenv` is on PATH. If Airflow is installed in a virtual environment itself and `virtualenv` is not installed in the environment the check might pass but `virtualenv` cannot be used as it is not present in the environment.

### What you think should happen instead

It should be checked if `virtualenv` is actually available in the environment.

```python
if importlib.util.find_spec("virtualenv") is None:
    raise AirflowException('PythonVirtualenvOperator requires virtualenv, please install it.')
```
https://stackoverflow.com/a/14050282

### How to reproduce

_No response_

### Operating System

Ubuntu 20.04

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### Deployment details

_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)

context_merge(context, self.op_kwargs, templates_dict=self.templates_dict)
        self.op_kwargs = self.determine_kwargs(context)
        return_value = self.execute_callable()
        if self.show_return_value_in_logs:
            self.log.info("Done. Returned value was: %s", return_value)
        else:
            self.log.info("Done. Returned value not shown")
        return return_value
    def determine_kwargs(self, context: Mapping[str, Any]) -> Mapping[str, Any]:
        return KeywordParameters.determine(self.python_callable, self.op_args, context).unpacking()
    def execute_callable(self) -> Any:
        """
        Calls the python callable with the given arguments.
        :return: the return value of the call.
        """
        return self.python_callable(*self.op_args, **self.op_kwargs)
class BranchPythonOperator(PythonOperator, SkipMixin):
    """
    A workflow can "branch" or follow a path after the execution of this task.
    It derives the PythonOperator and expects a Python function that returns
    a single task_id or list of task_ids to follow. The task_id(s) returned
    should point to a task directly downstream from {self}. All other "branches"
    or directly downstream tasks are marked with a state of ``skipped`` so that
    these paths can't move forward. The ``skipped`` states are propagated
    downstream to allow for the DAG state to fill up and the DAG run's state
    to be inferred.
    """
    def execute(self, context: Context) -> Any: