**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
failing the task
    :type retries: int
    :param retry_delay: delay between retries
    :type retry_delay: datetime.timedelta
    :param retry_exponential_backoff: allow progressive longer waits between
        retries by using exponential backoff algorithm on retry delay (delay
        will be converted into seconds)
    :type retry_exponential_backoff: bool
    :param max_retry_delay: maximum delay interval between retries
    :type max_retry_delay: datetime.timedelta
    :param start_date: The ``start_date`` for the task, determines
        the ``execution_date`` for the first task instance. The best practice
        is to have the start_date rounded
        to your DAG's ``schedule_interval``. Daily jobs have their start_date
        some day at 00:00:00, hourly jobs have their start_date at 00:00
        of a specific hour. Note that Airflow simply looks at the latest
        ``execution_date`` and adds the ``schedule_interval`` to determine
        the next ``execution_date``. It is also very important
        to note that different tasks' dependencies
        need to line up in time. If task A depends on task B and their
        start_date are offset in a way that their execution_date don't line
        up, A's dependencies will never be met. If you are looking to delay
        a task, for example running a daily task at 2AM, look into the
        ``TimeSensor`` and ``TimeDeltaSensor``. We advise against using
        dynamic ``start_date`` and recommend using fixed ones. Read the
        FAQ entry about start_date for more information.
    :type start_date: datetime.datetime
    :param end_date: if specified, the scheduler won't go beyond this date
    :type end_date: datetime.datetime
    :param depends_on_past: when set to true, task instances will run