There are a few different things that could be going on here:
One, the multicolinearity cannot be assessed based only on bivariate correlations. Consider the case where x1, x2, and x3 are all generated as independent normal random vanriables and x4 is the sum of x1, x2, and x3. There is definite colinearity with x4, but the bivariate colinearities are not strong (I did a quick example and the correlations ranged from 0.55 to 0.63). So you should use a better measure of colinearity than just bivariate correlations.
Two, it may be that x1, x2, and x3 explain quite a bit of the variation in the response variable and are independent of x4, so that without x1-x3 the effect size of x4 is small compared to the residual variation, but when you include x1-x3 the residual variation is much smaller and the effect size of x4 stays about the same, but is now large relative to the residual variation and therefore significant.
There are probably other explanations as well. You should really explore your data and all the relationships, then also consider the science behind the data, which model to use should be the one that makes the most sense scientifically.
But I do not understand what it means by "correct test for significance". Can someone explain what he's referring to?
If I were you I would post a comment to that answer by @EdM, otherwise, unless they actually see this question and answers themself, we can only make an informed guess. Having said that, what I think is meant by that statement, is that the model must include both the main effect and the interaction in order to make correct inferences. There could be some rare cases where it is not necessary to include the main effect, but as a good general rule, you should.
Now, looking at the output from your two models, the first thing I notice is:
the condition number is large, 2.17e+03. his might indicate that there are strong multicollinearity or other numerical problems
and also note that this warning is absent from the centered model.
One consequence of muticollinearity is that it can inflate standard errors, which increases p values. Your model contains an interaction which is a product of two other variables. Depending on the scale it might be the case that there is a high correlation between the interaction and the variables themselves and this could cause inflated p values. Centering variables often reduces correlation between them when nonlinear terms (such as an interaction) are included. Without access to the data itself it is hard to say if this is what is actually happening, but it's my best informed guess. Your first point of call should be a correlation matrix between all the predictors and this will give you a big hint if this is actually the cause.
However, further inspection of the output reveals that the R squared for both models is 1. This indicates that there is a problem somewhere. Without access to the data it is very difficult to see where that might be.
As to the reason why the estimates an p values for the main effects change after centering, first, note that in a model without an interaction term, mean-centering the variables will change only the intercept term. The coefficients and their standard errors for the other variables will be unchanged. However, in the presence of an interaction, the main effects no longer have the same interpretation. They are interpreted as the change in the outcome variable for a 1 unit change of the variable in question, when the other main effect that it is interacted with is at zero (or in the case of a categorical variable, its reference level). This implies that, after centering the variables, the estimates and their standard errors for the main effects that are involved in an interaction will change (and hence the p values too), because zero now has a different meaning after centering, but the estimate and the standard error for the interaction itself will remain unchanged. In other words the tests are different. Looking at the output, this is exactly what has happened.
Edit: To provide better understanding:
To understand the last point more fully we can write out the equations for two simple models, one without centering, and one with centering, with two predictors, $x_1$ and $x_2$ along with their interaction.
Firstly, the original (uncentered) model is:
$$\mathbb{E}[Y] = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_1x_2$$
Denote the centered variables by $z_1$ and $z_2$, such that
$$ \begin{align}
z_1 &= x_1 - \mu_1 \text{ and} \\
z_2 &= x_2 - \mu_2
\end{align} $$
where $\mu_1$ and $\mu_2$ are the means of $x_1$ and $x_2$ respectively. We can now write the model with centering in terms of the centered variables and the means of the uncentered variables:
$$\mathbb{E}[Y] = \beta_0 + \beta_1 (z_1 + \mu_1) + \beta_2 (z_2 + \mu_2) + \beta_3 (z_1 + \mu_1) (z_2 + \mu_2)$$
Expanding:
$$\mathbb{E}[Y] = \beta_0 + \beta_1 z_1 + \beta_1 \mu_1 + \beta_2 z_2 + \beta_2\mu_2 + \beta_3 z_1 z_2 +\beta_3 z_1 \mu_2 +\beta_3 z_2 \mu_1 + \beta_3 \mu_1 \mu_2 $$
Now, note that $\beta_1 \mu_1$, $\beta_2\mu_2$ and $\beta_3 \mu_1 \mu_2$ are all constant so these can be subsumed into a new intercept, $\gamma_0$, giving:
$$\mathbb{E}[Y] = \gamma_0 + \beta_1 z_1 + \beta_2 z_2 + \beta_3 z_1 z_2 +\beta_3 z_1 \mu_2 +\beta_3 z_2 \mu_1 $$
Rearranging this by factorizing by $z_1$, $z_2$ and $z_1 z_2$ we arrive at:
$$\mathbb{E}[Y] = \gamma_0 + z_1 (\beta_1 + \beta_3 \mu_2 ) + z_2 (\beta_2 + \beta_3 \mu_1) + z_1 z_2 \beta_3 $$
So, this is the simplified form of the regression model using the centered variables. We can immediately note that:
the intercept will be different from the uncentered model, since it is now equal to $ \gamma_0 = \beta_0 + \beta_1 \mu_1 +\beta_2\mu_2 +\beta_3 \mu_1 \mu_2$
the test for $z_1$ is comparing $\beta_1 + \beta_3 \mu_2$ to zero, or equivalently the equality of $\beta_1$ and $-\beta_3 \mu_2$, which will only be the same as the test for $\beta_1$ in the uncentered model if $\mu_2$ is zero, which obviously it isn't otherwise you wouldn't be centering $x_2$ in the first place.
similarly, the test for $z_2$ is comparing $\beta_2 + \beta_3 \mu_1$ to zero, which will only be the same as the test for $\beta_2$ in the uncentered model if $\mu_1$ is zero.
The test for $z_1 z_2$ is comparing $\beta_3$ to zero, which is the same as in the uncentered model.
Again, inspecting the output of both models, this is exactly what is happening.
To sum up, although the two models are the same, ie the centered model is just a re-parameterization of the uncentered model, the p values for the tests of the estimated coefficient for the main effects of the centered variables that are involved in the interaction, and the intercept, will be different, because they are testing different things. The p values for the tests of the estimated coefficients of the main effect which is not involved in an interaction, along with that for the interaction, will be unchanged. These are general results. In addition to this, in your particular data there could also be issues due to multicollinearity, and the fact that R-squared is reported as 1, is also suspicious.
Best Answer
Multicollinearity doesn't have to be terribly high for this to happen. It sounds like $X_1$ is correlated with $X_4$, and so when $X_4$ is not included in the model, $X_1$ takes credit for the variability in $Y$ that $X_4$ is responsible for. When $X_4$ is included, the model recognizes that $X_4$ and not $X_1$ is responsible for the effect and switches to attributing the effect to $X_4$.