**Apache Airflow version**: 2.0.0rc3

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**: Linux
- **OS** (e.g. from /etc/os-release): Ubuntu
- **Kernel** (e.g. `uname -a`): 
- **Install tools**:
- **Others**:

**What happened**:

After shutting down the scheduler, while tasks were in running state, trying to restart the scheduler results in pk violations..

```[2020-12-15 22:43:29,673] {scheduler_job.py:1293} ERROR - Exception when executing SchedulerJob._run_scheduler_loop
Traceback (most recent call last):
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context
    cursor, statement, parameters, context
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 593, in do_execute
    cursor.execute(statement, parameters)
psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "dag_run_dag_id_run_id_key"
DETAIL:  Key (dag_id, run_id)=(example_task_group, scheduled__2020-12-14T04:31:00+00:00) already exists.
```


**What you expected to happen**:

Scheduler restarts and picks up where it left off.

**How to reproduce it**:

Set example dag ( I used task_group) to schedule_interval `* * * * *` and start the scheduler and let it run for a few minutes. 
Shut down the scheduler
Attempt to restart the scheduler

**Anything else we need to know**:
I came across this doing testing using the LocalExecutor in a virtual env. If no else is able to reproduce it, I'll try again in a clean virtual env.

airflow/serialization/serialized_objects.py
"""
    A JSON serializable representation of DAG.
    A stringified DAG can only be used in the scope of scheduler and webserver, because fields
    that are not serializable, such as functions and customer defined classes, are casted to
    strings.
    Compared with SimpleDAG: SerializedDAG contains all information for webserver.
    Compared with DagPickle: DagPickle contains all information for worker, but some DAGs are
    not pickle-able. SerializedDAG works for all DAGs.
    """
    _decorated_fields = {'schedule_interval', 'default_args', '_access_control'}
    @staticmethod
    def __get_constructor_defaults():  
        param_to_attr = {
            'concurrency': '_concurrency',
            'description': '_description',
            'default_view': '_default_view',
            'access_control': '_access_control',
        }
        return {
            param_to_attr.get(k, k): v.default
            for k, v in signature(DAG.__init__).parameters.items()
            if v.default is not v.empty
        }
    _CONSTRUCTOR_PARAMS = __get_constructor_defaults.__func__()  
    del __get_constructor_defaults
    _json_schema = load_dag_schema()
    @classmethod
    def serialize_dag(cls, dag: DAG) -> dict: