To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints algebraically.

Let's denote:
- \(x_1\) as the number of zero gravity seats sold,
- \(x_2\) as the number of standard seats sold.

The objective is to maximize profit, given that each zero gravity seat yields a profit of $55 and each standard seat yields a profit of $25. Thus, the objective function can be written as:
\[ \text{Maximize:} \quad 55x_1 + 25x_2 \]

Now, let's consider the constraints based on the problem description:
1. The total number of seats (both zero gravity and standard) cannot exceed 250.
\[ x_1 + x_2 \leq 250 \]
2. At least 40 seats must be reserved as zero gravity seats.
\[ x_1 \geq 40 \]
3. At least 4 times as many people prefer to sit in standard seats than in zero gravity seats.
\[ x_2 \geq 4x_1 \]

Additionally, since we cannot sell a negative number of seats, both \(x_1\) and \(x_2\) must be non-negative:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

Given these definitions, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'number of zero gravity seats sold'), ('x2', 'number of standard seats sold')],
    'objective_function': '55*x1 + 25*x2',
    'constraints': ['x1 + x2 <= 250', 'x1 >= 40', 'x2 >= 4*x1', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we can write the following code:

```python
from gurobipy import *

# Create a new model
m = Model("Movie_Theatre_Seating")

# Define variables
x1 = m.addVar(lb=0, name="zero_gravity_seats")
x2 = m.addVar(lb=0, name="standard_seats")

# Set the objective function
m.setObjective(55*x1 + 25*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 250, "total_seats_limit")
m.addConstr(x1 >= 40, "zero_gravity_minimum")
m.addConstr(x2 >= 4*x1, "standard_vs_zero_gravity_ratio")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal number of zero gravity seats: {x1.x}")
    print(f"Optimal number of standard seats: {x2.x}")
    print(f"Maximum profit: ${m.objVal:.2f}")
else:
    print("No optimal solution found")
```