I apologize in advance for the really long answer, I just don't want to assume any level of familiarity with linear regression. Also, the answer touches on 2-3 different topics, so I wanted to cover all bases.
The answer to your question has to do with what are $B1$ and $B2$. Linear regression is trying to find the unique $\hat{\beta}$ that minimizes the least squares, i.e. in your case: $$\hat{B_0}, \hat{B_1}, \hat{B_2} = \arg\min_{B_0, B_1, B_2} \sum_{i=1}^n\left( sale - B_0 - B_1TV - B_2online\right)^2 $$
or in vector notation:
$$ \hat{\beta} = \arg\min_{\beta\in\mathbb{R}^3} || y - X\beta||^2_2$$ where $y$ is a $n\times1$ vector with your $sale$ data, and $X$ is a $n\times3$ matrix with your variables, i.e. the first column has all 1s, the second column has your $TV$ data, and the third has the $online$ data.
The way to solve this system of equations is: $$\hat{\beta} = (X^TX)^{-1}X^Ty$$
If your data satisfy the full rank assumption, then your $\hat{\beta}$ is unique, because $(X^TX)^{-1}$ is unique. So in the end, it's nothing more than just solving a system of equations.
Where does correlation come into play? If the correlation between $TV$ and $online$ is 1, then your data do not satisfy the full rank assumption, so the matrix above is not invertible, and there's not unique solution. Practically, this means that the algorithm doesn't know where to assign predictive/explanatory power. If it's close to 1 (e.g. > .9), the computer might have trouble finding the exact inverse, so be careful there. If it's less that that, but still high this will probably inflate your standard errors (multicollinearity).
Finally, why are both variables significant in the univariate cases, but not in the multiple regression? Exactly, because they're correlated, the each have a direct and an indirect effect (through the other variable) on $y$. By including only one of them, you're picking up both effects, but if you include both, you're picking up their direct effects (plus any indirect through other missing variables). Assuming that both variables have some effect on $y$ and they correlate with each other, you should include both of them in the regression, because otherwise you're introducing bias (omitted variable bias).
