### Apache Airflow version

main (development)

### What happened

Task failing with error "missing upstream values" when running a dag with dynamic task group mapping
error -
```
[2022-11-23, 15:50:07 UTC] {abstractoperator.py:456} ERROR - Cannot expand <Task(_PythonDecoratedOperator): increment_and_verify.hello_there> for run manual__2022-11-23T15:49:59.771915+00:00; missing upstream values: ['x']
```

### What you think should happen instead

It should succeed 

### How to reproduce

Run the below dag code 
```
from airflow import DAG
from airflow.models.taskinstance import TaskInstance
from airflow.operators.dummy import DummyOperator
from datetime import datetime, timedelta
from airflow.decorators import task, task_group
with DAG(
    dag_id="taskmap_taskgroup",
    tags=["AIP_42"],
    start_date=datetime(1970, 1, 1),
    schedule_interval=None,
) as dag:

    @task
    def onetwothree():
        return [1,2,3]

    @task
    def hello_there(arg):
        print(arg)

    @task_group
    def increment_and_verify(x):
        hello_there(x)

    increment_and_verify.expand(x=onetwothree())>>DummyOperator(task_id="done")
```

### Operating System

mac os

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""
        Returns a map of task instance key to task instance object for the tasks to
        run in the given dag run.
        :param dag_run: the dag run to get the tasks from
        :param session: the database session object
        """
        tasks_to_run = {}
        if dag_run is None:
            return tasks_to_run
        
        self.reset_state_for_orphaned_tasks(filter_by_dag_run=dag_run, session=session)
        
        dag_run.refresh_from_db()
        make_transient(dag_run)
        dag_run.dag = dag
        info = dag_run.task_instance_scheduling_decisions(session=session)
        schedulable_tis = info.schedulable_tis
        try:
            for ti in dag_run.get_task_instances(session=session):
                if ti in schedulable_tis:
                    ti.set_state(TaskInstanceState.SCHEDULED)
                if ti.state != TaskInstanceState.REMOVED:
                    tasks_to_run[ti.key] = ti
            session.commit()
        except Exception:
            session.rollback()
            raise
        return tasks_to_run
    def _log_progress(self, ti_status):