Wrong reward at terminal date when training with SB3

**Describe the bug**
For StockTradingEnv when training in SB3 VecEnv (using get_sb_env), at the terminal date(let's say day X), it doesn't calculate reward since training data doesn't contain the next date closed price.
But when SB3 algorithm conducts collect_rollouts, it adds the previous reward (day X-1's reward) into rollout_buffer.

**To Reproduce**
Steps to reproduce the behavior:
1. pick a short period training data to quickly reach terminal date when debuging , eg. 5days in total.
2. break point after rollout_buffer.add() in on_policy_algorithm.py(SB3 module) line 232
3. stepping until after day 5 (add the 4th experience)
4. see the rollout_buffer variable (the reward[3] is the same as the previous one)

**Expected behavior**
after I examined SB3 code, it provides a solution for calculating the reward when next state is unobservable in episode. it use prediction value with discount as reward, while I'm not sure about its mathematic meaning.
```
# see GitHub issue #633
for idx, done in enumerate(dones):
if (
done
and infos[idx].get("terminal_observation") is not None
and infos[idx].get("TimeLimit.truncated", False)
):
terminal_obs = self.policy.obs_to_tensor(infos[idx]["terminal_observation"])[0]
with th.no_grad():
terminal_value = self.policy.predict_values(terminal_obs)[0] # type: ignore[arg-type]
rewards[idx] += self.gamma * terminal_value
```

But it requires the Env return "terminated = False, truncated = True." (while StockTradingEnv return "terminated = True, truncated = False:) # In SB3, truncated and terminated are mutually exclusive.

I think in stock trading scenario, the last day in training data should be a time limit truncation, but not a terminal, because it has future reward (value), unless it holds zero shares and the policy will not buy anymore at that state.

**Screenshots**

**Additional context**
I simply change the return in terminal case in the StockTradingEnv code, line 300.
`return self.state, 0.0, False, True, {}`. # reward = 0, could cause some issue

but I didn't consider the impact on ElegantRL, Ray and Portfolio Management scene.