If know that if $(X_n)_{n\in\mathbb N}$ is a stochastic process, then $X_n$ can be written as $X_n=M_n+A_n$ where $(M_n)$ is a Martingale and $(A_n)$ is an adapted and predictable process.
I know that for $(X_t)_{t\geq 0}$ being a semi-martingale, $$X_t=M_t+A_t$$ where $(M_t)$ is a local martingale and $A_t$ is an adapted and predictable process.
Question
So in discret version, such a decomposition exist for any process, whereas the theorem I have in continuous version is only for semimartingale. But at the end, does such decomposition works for any process as well like in the discrete version, or $(X_t)$ must be a semimartingale ?
The semi-martingale part of it is important. Recall that there's a tight relationship between local martingales and semi martingales (every local submartingale is a semimartingale). One verion of the theorem you reference is the Doob-Meyer decomposition that states that $X$ must be a class $(DL)$ right-continuous submartingale in order for a right-continuous martingale $M$ and a right-continuous increasing predictable process $A$ to exist, such that $X_t=M_t+A_t$. In this framework, $X$ being of $(DL)$ class requires that $\forall \alpha\geq 0$, denoting $\tau$ a given stopping time, $\{X_\tau: \tau\leq \alpha\}$ is uniformly integrable. The importance of this local property has to do with the usual technique in continuous time of proving results for local martingales on stopping times and then extending the result to any time (here, the uniform integrability plays a role in granting that the expectations are finite while extending through stopping times).
In your case, by not working with semi-martingales, your process might not have the local characteristics needed to make the Doob-Mayer decomposition be generally true for all times.
Here, you can find more information on $(DL)$ submartingales and the Doob-Meyer Decomposition.