To solve this problem, we first need to define the variables and constraints based on the given information.

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

The objective is to maximize profit. The profit made from each zero gravity seat is $55, and from each standard seat is $25. Therefore, the total profit \(P\) can be represented as:
\[ P = 55Z + 25S \]

Now, let's consider the constraints:
1. **Total Capacity Constraint**: The theatre can hold at most 250 people.
\[ Z + S \leq 250 \]
2. **Minimum Zero Gravity Seats Constraint**: At least 40 seats must be reserved for zero gravity seats.
\[ Z \geq 40 \]
3. **Preference Constraint**: At least 4 times as many people prefer to sit in standard seats than in zero gravity seats.
\[ S \geq 4Z \]

To ensure that we're dealing with whole numbers of seats (since you can't sell a fraction of a seat), both \(Z\) and \(S\) should be integers.

Now, let's translate these constraints into Gurobi code in Python:

```python
from gurobipy import *

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

# Define the variables
Z = m.addVar(vtype=GRB.INTEGER, name="Zero_Gravity_Seats")
S = m.addVar(vtype=GRB.INTEGER, name="Standard_Seats")

# Set the objective function: Maximize profit
m.setObjective(55*Z + 25*S, GRB.MAXIMIZE)

# Add constraints
m.addConstr(Z + S <= 250, "Total_Capacity")
m.addConstr(Z >= 40, "Minimum_Zero_Gravity_Seats")
m.addConstr(S >= 4*Z, "Preference")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Zero Gravity Seats: {Z.x}")
    print(f"Standard Seats: {S.x}")
    print(f"Total Profit: ${55*Z.x + 25*S.x:.2f}")
else:
    print("No optimal solution found")
```