(1) is certainly not true for general signed measures $\mu$.  However, if we restrict to signed measures with $\mu(X)=0$, then it is true with a factor of $2$, i.e.
$$\|\mu\|_{TV} = 2 \sup_{A \in \mathcal{B}} |\mu(A)| \tag{*}.$$
That is, in this special case, the leftmost inequality in (2) is attained.


For one inequality, let $X = B^+ \cup B^-$ be the Hahn decomposition for $\mu$.  Note that $\|\mu\|_{TV} = \mu(B^+) - \mu(B^-)$, while $\mu(X) = \mu(B^+) + \mu(B^-) = 0$ so that $\mu(B^+) = -\mu(B^-) = \frac{1}{2} \|\mu\|_{TV}$.   Hence taking $A = B^+$ shows the $\le$ inequality in (*).

Conversely, for any $A \in \mathcal{B}$ we have $$\mu(A) = \mu(A \cap B^+) + \mu(A \cap B^-) \le \mu(A \cap B^+) \le \mu(B^+) = \frac{1}{2} \|\mu\|_{TV}$$
and similarly $\mu(A) \ge -\frac{1}{2} \|\mu\|_{TV}$, so that $|\mu(A)| \le \frac{1}{2} \|\mu\|_{TV}$.  This shows the $\ge$ inequality.

In particular, taking $\mu = P-Q$ where $P,Q$ are both probability measures, we see that $d_{TV}(P,Q)$ as defined by (3) is exactly half of $\|P-Q\|_{TV}$.  So the definitions are the same, up to a constant factor of 2.

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Your equation (4) is also off by a factor of 1/2.  The identity
$$\|\mu\|_{TV} = \sup_{\|f\|_\infty \le 1} \int f\,d\mu$$
is true for every signed measure.  To see one direction, write
$$\int f\,d\mu = \int f\,d\mu^+ - \int f\,d\mu^- \le \mu^+(X) + \mu^-(X) = \|\mu\|_{TV}.$$
For the opposite inequality, take $f = 1_{B^+} - 1_{B^-}$.