A Reward is a number returned at a certain step of the Markov Decision Process. If you arrange things in sequence over a whole time step s,a,r,s' for state, action, reward, next state, then the reward r is allowed to depend on all three of s,a,s', and it can also be from a random distribution of real numbers or just a single number.
State values are a way to measure longer-term benefits of being in a state, they are also called the expected return for an agent starting from that state and following a particular policy.
Therefore, we can see state values composed of many rewards weighted by their probability of occurring in the future.
