Both players are represented by the exact same network with the exact same weights(similar to AplhaGO, AlphaGoZero and AlphaZero). So, they will both behave identical. Because you only have a single network, MuZero can not learn two different policies, but only one.
You can also think of this in the following way: MuZero actually learn to play only with white(or black, but just one of them) without knowing to play with the other color (at least multiple implementations of the previous algorithms like AlphaGo Zero and AlphaZero that are similar to MuZero are doing exactly this). So, in order to trick it to also be able to play with the other color, when the network need to play with black, you just flip the colors on the table so that black becomes white(and white becomes black) and the network knows what to do. After choosing the move, you flip the whole thing back and that is usually how it is done. So, from the perspective of your network, it will always play white, but because you do the flipping of the colors you can actually put them to play against each other without them knowing that.
Even without using this trick of flipping the color of the table, by doing the MCTS simulations, you will have for each state the statistics for the actions, and usually as you do more simulations, this statistics show you which actions are the best in each state. And when you train, you try to imitate this. So, your network will learn in each state which actions are the best, and this is the reason why it learns to take the best possible action in each state.