I think you do not need to do anything with it. Essentially you are asking how to deal with "correlated" features, i.e., two random variables $X_1$ and $X_2$ they are not independent where $P(X_1,X_2)!=P(X_1)P(X_2)$. This case is very normal in most real world problems.
For example, you want to predict a person's income, it is very common to use both age and education level as features, and age and education level are not independent, e.g., higher education usually indicates the person is older. Another example would be predicting a person's healthy status. It is very common to use both height and weight. Although we know height and weight has really strong correlation.
In your example, the only difference is you have discrete random variables and the two examples I mentioned above are continuous random variables. For your case, Let's use $X_1$ to represent spouse's age and $X_2$ for is married. You will have, say $P(X_1|X_2=1)$ is normal distribution, while $P(X_1|X_2=0)=0$.
As well as you do not have highly correlated features, such as identical features, e.g., $P(X_1)=P(X_2)$ / "weight in lb and weight in kg", you would not have too much problems. If you do have "collinearity problem", you can drop some "repetitive" features or use Principal component analysis or use regularization