Decision tree where identical set of features results in different outcomes
The task in hand can be modeled as a classification problem.
Meaning based on some given features we aim to predict the 'right' class of the outcome.
Once trained, most classification models, decisions tree included, will predict the most probable class given a set of input features.
Learning what is the most probable class, is usually done in the 'fitting' step of model training.
In your example the set of features results in 2 possible classes(hard, none) meaning that there is a 50% chance being either.
The classifier is just as wrong as its right in predicting the 'none' label.

** Some decisions tree implementations include an option to predict the probability for each class, and predict a class only if a specific confidence threshold is defined.