Decision tree not using all features from training dataset
When a feature is not that informative of your target, the algorithm can choose not to use it. This can be for two reasons:


All the information is already in the other features, meaning it adds nothing to include it
There is some regularization going on, meaning your algorithm punishes complexity if it doesn't add enough


It can also be that the regularization is very explicit, meaning there is a maximum depth set as hyperparameter. In this case the other features are more informative and useful for your task.