## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables as follows:
- $x_1$ represents the number of zero gravity seats.
- $x_2$ represents the number of standard seats.

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

The constraints based on the problem description are:
1. The movie theatre can hold at most 250 people: $x_1 + x_2 \leq 250$
2. The arena reserves a minimum of 40 seats to be zero gravity seats: $x_1 \geq 40$
3. At least 4 times as many people prefer to sit in standard seats than zero gravity seats: $x_2 \geq 4x_1$

## Symbolic Representation in JSON Format

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

## Gurobi Code in Python

```python
import gurobi

def solve_movie_theatre_seating():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="zero_gravity_seats", lb=0, vtype=gurobi.GRB.INTEGER)  # Zero gravity seats
    x2 = model.addVar(name="standard_seats", lb=0, vtype=gurobi.GRB.INTEGER)  # Standard seats

    # Objective function: Maximize profit
    model.setObjective(55 * x1 + 25 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 250, name="total_seats")  # Total seats constraint
    model.addConstr(x1 >= 40, name="min_zero_gravity")  # Minimum zero gravity seats
    model.addConstr(x2 >= 4 * x1, name="std_vs_zero_gravity")  # Standard vs zero gravity preference

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution found.")
        print(f"Number of zero gravity seats: {x1.varValue}")
        print(f"Number of standard seats: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_movie_theatre_seating()
```