What is the reasoning behind the string of equality $P(x_{(n)} \le t) = P(X_i \le t, \ \text{for each $i$}) = \{\Phi(t - \theta)\}^n$? I am currently studying the textbook In All Likelihood by Yudi Pawitan. Example 2.4 of chapter 2.2 Examples says the following:

Example 2.4: Suppose $x$ is a sample from $N(\theta, 1)$; the likelihood of $\theta$ is
$$L(\theta) = \phi(x - \theta) \equiv \frac{1}{\sqrt{2\pi}} e^{-\frac{1}{2} (x - \theta)^2}.$$
The dashed curve in Figure 2.3(d) is the likelihood based on observing $x = 2.45$.
Suppose it is known only that $0.9 < x < 4$; then the likelihood of $\theta$ is
$$L(\theta) = P(0.9 < X < 4) = \Phi(4 - \theta) - \Phi(0.9 - \theta),$$
where $\Phi(x)$ is the standard normal distribution function. The likelihood is shown in solid line in Figure 2.3(d).
Suppose $x_1, \dots, x_n$ are an identically and independently distributed (iid) sample from $N(\theta, 1)$, and only the maximum $x_{(n)}$ is reported, while the others are missing. The distribution function of $x_{(n)}$ is
$$\begin{align} F(t) &= P(x_{(n)} \le t) \\ &= P(X_i \le t, \ \text{for each $i$}) \\ &= \{\Phi(t - \theta)\}^n. \end{align}$$
So, the likelihood based on observing $x_{(n)}$ is
$$L(\theta) = p_\theta (x_{(n)}) = n\{ \Phi(x_{(n)} - \theta)\}^{n - 1} \phi(x_{(n)} - \theta).$$

What is the reasoning behind the string of equality $P(x_{(n)} \le t) = P(X_i \le t, \ \text{for each $i$}) = \{\Phi(t - \theta)\}^n$?

Preliminary note: There is inconsistency in the use of lower/upper case notation for random variables in the question.  I have chosen to correct this in the answer so my capitalisation is different to the question.
The first equation comes from the fact that $X_{(n)} = \max \{ X_1,...,X_n \}$, which gives the logical equivalence:
$$X_{(n)} \leqslant t
\quad \quad \iff \quad \quad 
\begin{matrix}
X_1 \leqslant t, \\
X_2 \leqslant t, \\
\vdots \\
X_n \leqslant t. \\
\end{matrix}$$
In simple terms, this is saying that if the maximum of $X_1,...,X_n$ is no greater than $t$ then there cannot be any value in $X_1,...,X_n$ that is greater than $t$, and likewise, if there is no value in $X_1,...,X_n$ that is greater than $t$ then the maximum of $X_1,...,X_n$ is no greater than $t$.  Once you impose this logical equivalence for the underlying events, you then have:
$$\mathbb{P}(X_{(n)} \leqslant t) = \mathbb{P}(X_1 \leqslant t,X_2 \leqslant t,...,X_n \leqslant t).$$

The second equation comes from the fact that the random variables $X_1,...,X_n$ were stipulated to be independent and identically distributed (IID) normal random variables with mean $\theta$ and unit variance, which means that:
$$\begin{align}
\mathbb{P}(X_1 \leqslant t,X_2 \leqslant t,...,X_n \leqslant t)
&= \prod_{i=1}^n \mathbb{P}(X_i \leqslant t) \\[6pt]
&= \prod_{i=1}^n \mathbb{P}(X_i-\theta \leqslant t-\theta) \\[6pt]
&= \prod_{i=1}^n \Phi(t-\theta) \\[12pt]
&= [\Phi(t-\theta)]^n. \\[6pt]
\end{align}$$
(The penultimate step here comes from the fact that $X_1-\theta,...,X_n-\theta \sim \text{IID N}(0,1)$ and $\Phi$ is the notation used for the CDF of the standard normal distribution.)
