**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
'partitionType': 'DAY'
            },
            dag=dag)
    .. seealso::
        For more detail on job submission have a look at the reference:
        https://cloud.google.com/dataflow/pipelines/specifying-exec-params
    :param jar: The reference to a self executing DataFlow jar (templated).
    :type jar: str
    :param job_name: The 'jobName' to use when executing the DataFlow job
        (templated). This ends up being set in the pipeline options, so any entry
        with key ``'jobName'`` in ``options`` will be overwritten.
    :type job_name: str
    :param dataflow_default_options: Map of default job options.
    :type dataflow_default_options: dict
    :param options: Map of job specific options.The key must be a dictionary.
        The value can contain different types:
        * If the value is None, the single option - ``--key`` (without value) will be added.
        * If the value is False, this option will be skipped
        * If the value is True, the single option - ``--key`` (without value) will be added.
        * If the value is list, the many options will be added for each key.
          If the value is ``['A', 'B']`` and the key is ``key`` then the ``--key=A --key-B`` options
          will be left
        * Other value types will be replaced with the Python textual representation.
        When defining labels (``labels`` option), you can also provide a dictionary.
    :type options: dict
    :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.
    :type project_id: str
    :param location: Job location.
    :type location: str