**Apache Airflow version**:
v2.0.0

**What happened**:

Custom operators inheriting from DummyOperator are go in to a scheduled state, where as DummyOperator itself is set straight to success.

**What you expected to happen**:

All operators should be successfully executed.

**How to reproduce it**:

```py
from datetime import datetime
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator

class MyJob(DummyOperator):
    template_fields_renderers = {
        "body": "json"
    }
    template_fields = ("body",)

    def __init__(self, body, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.body = body

with DAG(
    dag_id="test",
    schedule_interval=None,
    start_date=datetime(2020, 8, 13),
) as dag:
    MyJob(task_id="aaaa", body={"aaa": "bbb"})

```

**Anything else we need to know**:
This is a known problem that we will fix, see: https://github.com/apache/airflow/pull/10956#discussion_r502492775
airflow/models/baseoperator.py
):
    r"""
    Set downstream dependencies for all tasks in from_tasks to all tasks in to_tasks.
    .. code-block:: python
        cross_downstream(from_tasks=[t1, t2, t3], to_tasks=[t4, t5, t6])
    is equivalent to::
        t1 ---> t4
           \ /
        t2 -X -> t5
           / \
        t3 ---> t6
    .. code-block:: python
        t1.set_downstream(t4)
        t1.set_downstream(t5)
        t1.set_downstream(t6)
        t2.set_downstream(t4)
        t2.set_downstream(t5)
        t2.set_downstream(t6)
        t3.set_downstream(t4)
        t3.set_downstream(t5)
        t3.set_downstream(t6)
    :param from_tasks: List of tasks to start from.
    :type from_tasks: List[airflow.models.BaseOperator]
    :param to_tasks: List of tasks to set as downstream dependencies.
    :type to_tasks: List[airflow.models.BaseOperator]
    """
    for task in from_tasks:
        task.set_downstream(to_tasks)
@attr.s(auto_attribs=True)
class BaseOperatorLink(metaclass=ABCMeta):