**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/operators/dataflow.py
:param gcp_conn_id: The connection ID to use connecting to Google Cloud
        Platform.
    :type gcp_conn_id: str
    :param delegate_to: The account to impersonate, if any.
        For this to work, the service account making the request must have
        domain-wide delegation enabled.
    :type delegate_to: str
    :param poll_sleep: The time in seconds to sleep between polling Google
        Cloud Platform for the dataflow job status while the job is in the
        JOB_STATE_RUNNING state.
    :type poll_sleep: int
    It's a good practice to define dataflow_* parameters in the default_args of the dag
    like the project, zone and staging location.
    .. seealso::
        https://cloud.google.com/dataflow/docs/reference/rest/v1b3/LaunchTemplateParameters
        https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment
    .. code-block:: python
       default_args = {
           'dataflow_default_options': {
               'zone': 'europe-west1-d',
               'tempLocation': 'gs://my-staging-bucket/staging/',
               }
           }
       }
    You need to pass the path to your dataflow template as a file reference with the
    ``template`` parameter. Use ``parameters`` to pass on parameters to your job.
    Use ``environment`` to pass on runtime environment variables to your job.
    .. code-block:: python
       t1 = DataflowTemplateOperator(
           task_id='dataflow_example',