Your first equation is the definition of any state value function, so it must also be definition of the optimal state value function $v_*$.
The second equation is the definition of $v_*$ in terms of the state-action value function $\color{green}{q_*}$.
In reality, the first equation is also the definition of $v_*$ in terms of $\color{green}{q_*}$, which is what you want to see :)
First, note that
\begin{align}
v_\pi(s)
&=
\sum_a \pi(a \mid s)
\color{blue}{\sum_{s'} \sum_r p(s', r|s, a) [r + \gamma v_\pi(s')]} \\
&= \sum_a \pi(a \mid s) \color{blue}{q_\pi(s, a)} \tag{1}\label{1}.
\end{align}
Now, we denote the optimal state value function as $v_{\pi_*} = v_*$. If we plug $v_*$, $\pi_*$ and $\color{green}{q_*}$ in the equation above, we get your first equation, but we write it as
\begin{align}
v_*(s)
&= \sum_a \pi_*(a \mid s) \color{green}{q_*(s, a)} \tag{2}\label{2}.
\end{align}
For finite MDPs, the optimal policy is deterministic, i.e. it chooses one action (the optimal one) with probability $1$, so that means that \ref{2} can be written as
\begin{align}
v_*(s)
&= 0 \color{green}{q_*(s, a_1)} + \cdots + 1 \color{green}{q_*(s, a_*)} + 0 \color{green}{q_*(s, a_N)} \\
&= \color{green}{q_*(s, a_*)} \\
&= \max_a \color{green}{q_*(s, a)} \\
&=\max_a \color{green}{ \sum_{s'} \sum\limits_r p(s', r|s,a) [r + \gamma v_{*}(s')]}
\tag{3}\label{3},
\end{align}
where $a_* = \text{argmax}_a\pi_*(a \mid s) = \text{argmax}_a \color{green}{q_*(s, a)}$ is the optimal action. By definition, the optimal action in state $s$ is the one that leads to the highest expected return. See also this answer.
Finally, note that optimal value functions are unique for finite MPDs, so $\color{green}{q_*}$ and $v_*$ are unique.