Ensemble Model using Stacking I learned that building an ensemble model using stacking is done by training a meta-model on the predictions of $n$ other models in order to combine the predictions and try to enhance the performance. But can we in addition add the original input features to teach the model a relation between specific input features and the models predictions, hence, increase the accuracy?

You certainly can. Whether you train your meta-model on just the base model predictions, predictions + original data, transformations of one or both of these, etc. can be viewed as just another hyper-parameter you can tune in your meta-model.
In Python, StackingClassifier has an argument to allow you to do exactly what you've suggested (passthrough = True).
When you view your base model out-of-sample predictions as "just another feature", the possibilities are pretty endless.
