**What happened**:

With the new Backport providers, when calling `DataflowCreatePythonJobOperator` with an empty `py_rerquirments` Dataflow commands always launch inside a virtualenv.

**What you expected to happen**:

The Dataflow command should run locally.


airflow/providers/google/cloud/hooks/dataflow.py
:type dataflow: str
        :param py_options: Additional options.
        :type py_options: List[str]
        :param py_interpreter: Python version of the beam pipeline.
            If None, this defaults to the python3.
            To track python versions supported by beam and related
            issues check: https://issues.apache.org/jira/browse/BEAM-1251
        :param py_requirements: Additional python package(s) to install.
            If a value is passed to this parameter, a new virtual environment has been created with
            additional packages installed.
            You could also install the apache-beam package if it is not installed on your system or you want
            to use a different version.
        :type py_requirements: List[str]
        :param py_system_site_packages: Whether to include system_site_packages in your virtualenv.
            See virtualenv documentation for more information.
            This option is only relevant if the ``py_requirements`` parameter is passed.
        :type py_interpreter: str
        :param append_job_name: True if unique suffix has to be appended to job name.
        :type append_job_name: bool
        :param project_id: Optional, the GCP project ID in which to start a job.
            If set to None or missing, the default project_id from the GCP connection is used.
        :param on_new_job_id_callback: Callback called when the job ID is known.
        :type on_new_job_id_callback: callable
        :param location: Job location.
        :type location: str
        """
        name = self._build_dataflow_job_name(job_name, append_job_name)
        variables['job_name'] = name
        variables['region'] = location
        def label_formatter(labels_dict):