Simple linear regression vs Multiple Linear regression interpretation Suppose we have a multiple linear regression model with two predictors, $X_1$ and $X_2$:
$$Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + \epsilon.$$
We can interpret $\beta_1$ as the expected increase in $Y$ with a unit increase in $X_1$ when $X_2$ is held constant. This is because $\beta_1$ is the partial derivative of the expected value of $Y$ with respect to $X_1$.
Further, suppose that we also compute the simple linear regression of $Y$ against $X_1$:
$$Y = b_0 + b_1X_1 + \epsilon.$$
Then I've seen by some authors that: $b_1$ is the expected increase in $Y$ with a unit increase in $X_1$ without holding $X_2$ constant.
But I really don't see the last point because for me in the simple linear regression is  like holding constant $X_2$ by giving it a zero value.
So why do they say the in the simple linear regression all other predictors not considered are not constant?
I would really appreciate if you can help me clarify this idea.

For the most part, you should read my answer to: Is there a difference between 'controlling for' and 'ignoring' other variables in multiple regression?, of which, this is nearly a duplicate.

To address your explicit question more directly, $X_2$ is not being held constant.  What you have done is set $\beta_2 = 0$, not adjust the data to account for what they would be like if $X_2$ were $0$ for all data in the dataset.
Unless $X_1$ and $X_2$ were perfectly uncorrelated in your dataset, controlling for $X_2$ would amount to shifting the $X_1$ values to some degree.  As a result, the estimated $\hat\beta_1$s between the two models would differ.
