My question is, what is the null hypothesis in this likelihood ratio test?
Under the null for that particular test as described, all the parameters are the same (which is why you were fitting a single Weibull to all the data under that case) -- under the shape-scale parameterization
$$f(x;\lambda,\theta) =\frac{\theta}{\lambda}\left(\frac{x}{\lambda}\right)^{\theta-1}e^{-(x/\lambda)^{\theta}} \: \mathbb{I}_{x\geq0}\,,\quad \theta,\lambda>0$$
the null would be that both the shape and scale parameters ($\theta$ and $\lambda$ respectively) are the same, which you might write as:
$$H_0: \theta_1=\theta_2\, , \: \lambda_1=\lambda_2$$
It's your null and alternative hypotheses that determine the likelihood ratio test that you do, not the other way around. If that's not the null you wanted to test you have to set the test up differently. The test takes the ratio of likelihoods under the null and alternative (and asymptotically, minus twice its log will be chi-squared distributed under fairly broad conditions).
I get a P-value=0.4258827 and I don't know whether this means that the 2 Weibull distributions come from the same distribution or not.
Note that you don't state a significance level anywhere. You shouldn't even do the calculations for a test until you have picked one, and until you're clear what your rejection rule will be.
(If you're asking here how to interpret a p-value or what a p-value greater than your significance level - assuming it is - means, we have many discussions on that topic which can be searched for. There's little value in repeating what's been said very well on the basic mechanics of how hypothesis tests work. Start with Wikipedia on statistical hypothesis testing under "An alternative process", and there's some discussion here and here for example. Since explanations of what to do are easy to find I assume your actual issue is somewhat different.)
If you understand that you don't reject (presuming your significance level is lower than your p-value here), but are asking about how to interpret the non-rejection, note that failure to reject the null does not mean that the two distributions are the same (as your question suggests).
It means there's no clear indication that they're different above what you'd be able to explain as due to random variation. Which is to say that the data are reasonably consistent with them having come from the same Weibull. This is not at all the same as being able to assert that they actually do.
It may in that situation be reasonable to act as if they're all drawn from the same distribution, but we don't know it to be so.
