>>11663315no, the 2nd equation is not saying anything in itself, its just a definition. However, Q-learning does end up using that later on(which I'll explain below).
The Q definition above states that the action value of state s and action a under policy pi is the expected value of the 1 step return.
The value of a state is then the expectation over the action values, given that usually the policy you have is stochastic(that's the subscript on the expectation in the 2nd equation). Those are just definitions.
The equation you wrote states that the value of the state is the value of the best action that can be taken at that state. If you have a stochastic policy, this isn't at all intuitive
