In short, the two definitions are equivalent, and the one with finite sets is more convenient.
Claim. A family $\mathcal A=(A_i:i\in I)$ of events is independent, if and only if for all finite or countably infinite subsets $J$ of $I$,
$$
\mathbb P\left(\bigcap_{i\in J}A_i\right)=\prod_{i\in J}\mathbb P(A_i).
$$
Proof. Clearly this implies independence, since finite sets are 'finite or countably infinite'.
Conversely, for any countably infinite set $J$, let $\{J_n\}_{n=1}^{\infty}$ be an increasing sequence of finite sets such that $J=\bigcup_{n=1}^{\infty}J_n$. Then by continuity of measure,
$$
\mathbb P\left(\bigcap_{i\in J}A_i\right)=\lim_{n\to\infty}\mathbb P\left(\bigcap_{i\in J_n}A_i\right)=\lim_{n\to\infty}\prod_{i\in J_n}\mathbb P(A_i)=\prod_{i\in J}\mathbb P(A_i).
$$
So one may define independence using either finite or countably infinite sets. In practice, it is more convenient to use the definition with finite sets to verify that a given family of random variables is independent. Thus people stick with the first definition, since using countably infinite sets doesn't gain anything more.