**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
cls,
        encoded_group: Dict[str, Any],
        parent_group: Optional[TaskGroup],
        task_dict: Dict[str, BaseOperator],
    ) -> Optional[TaskGroup]:
        """Deserializes a TaskGroup from a JSON object."""
        if not encoded_group:
            return None
        group_id = cls._deserialize(encoded_group["_group_id"])
        kwargs = {
            key: cls._deserialize(encoded_group[key])
            for key in ["prefix_group_id", "tooltip", "ui_color", "ui_fgcolor"]
        }
        group = SerializedTaskGroup(group_id=group_id, parent_group=parent_group, **kwargs)
        group.children = {
            label: task_dict[val]
            if _type == DAT.OP  
            else SerializedTaskGroup.deserialize_task_group(val, group, task_dict)
            for label, (_type, val) in encoded_group["children"].items()
        }
        group.upstream_group_ids = set(cls._deserialize(encoded_group["upstream_group_ids"]))
        group.downstream_group_ids = set(cls._deserialize(encoded_group["downstream_group_ids"]))
        group.upstream_task_ids = set(cls._deserialize(encoded_group["upstream_task_ids"]))
        group.downstream_task_ids = set(cls._deserialize(encoded_group["downstream_task_ids"]))
        return group