### Apache Airflow version

2.6.3

### What happened

When the condition in ```ShortCircuitOperator``` is truthy it is returned at
https://github.com/apache/airflow/blob/a2ae2265ce960d65bc3c4bf805ee77954a1f895c/airflow/operators/python.py#L252
but when it is falsy, the function falls through without returning anything.

If ```multiple_outputs``` is set ```true``` (with for example ```@task.short_circuit(multiple_outputs=True```) ```_handle_output``` of
```DecoratedOperator``` at
https://github.com/apache/airflow/blob/a2ae2265ce960d65bc3c4bf805ee77954a1f895c/airflow/decorators/base.py#L242
does not test for None and raises at
https://github.com/apache/airflow/blob/a2ae2265ce960d65bc3c4bf805ee77954a1f895c/airflow/decorators/base.py#L255
This makes it impossible to pass a falsy value (i.e. an empty dictionary) so ```ShortCircuitOperator``` is unusable with ```multiple_outputs=true```


### What you think should happen instead

Probably the ```xcom_push``` should not be attempted with ```None```, or possibly the condition should be returned by ```ShortCircuitOperator``` even if it is falsy

### How to reproduce

```python
@task.short_circuit(multiple_outputs=True)
def test():
  return {}
```

### Operating System

Ubuntu 22.04.2 LTS

### Versions of Apache Airflow Providers

_No response_

### Deployment

Official Apache Airflow Helm Chart

### 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)

context_merge(context, self.op_kwargs, templates_dict=self.templates_dict)
        self.op_kwargs = self.determine_kwargs(context)
        return_value = self.execute_callable()
        if self.show_return_value_in_logs:
            self.log.info("Done. Returned value was: %s", return_value)
        else:
            self.log.info("Done. Returned value not shown")
        return return_value
    def determine_kwargs(self, context: Mapping[str, Any]) -> Mapping[str, Any]:
        return KeywordParameters.determine(self.python_callable, self.op_args, context).unpacking()
    def execute_callable(self) -> Any:
        """
        Calls the python callable with the given arguments.
        :return: the return value of the call.
        """
        return self.python_callable(*self.op_args, **self.op_kwargs)
class BranchPythonOperator(PythonOperator, SkipMixin):
    """
    A workflow can "branch" or follow a path after the execution of this task.
    It derives the PythonOperator and expects a Python function that returns
    a single task_id or list of task_ids to follow. The task_id(s) returned
    should point to a task directly downstream from {self}. All other "branches"
    or directly downstream tasks are marked with a state of ``skipped`` so that
    these paths can't move forward. The ``skipped`` states are propagated
    downstream to allow for the DAG state to fill up and the DAG run's state
    to be inferred.
    """
    def execute(self, context: Context) -> Any: