## Step 1: Define the Symbolic Representation
First, let's define the symbolic representation of the problem. We have two variables:
- $x_1$ representing the number of cheeseburgers
- $x_2$ representing the number of fries

The objective is to maximize profit, with a profit of $1.50 per cheeseburger and $1 per fries. So, the objective function is $1.50x_1 + 1x_2$.

## Step 2: List the Constraints
We have the following constraints:
1. $x_1 \geq 0$ (non-negativity constraint for cheeseburgers)
2. $x_2 \geq 0$ (non-negativity constraint for fries)
3. $x_1 \leq 110$ (demand constraint for cheeseburgers)
4. $x_2 \leq 80$ (demand constraint for fries)
5. $x_1 + x_2 \leq 150$ (capacity constraint)

## 3: Symbolic Representation in JSON Format
Here is the symbolic representation in the requested JSON format:

```json
{
    'sym_variables': [('x1', 'cheeseburgers'), ('x2', 'fries')],
    'objective_function': '1.50*x1 + 1*x2',
    'constraints': [
        'x1 >= 0',
        'x2 >= 0',
        'x1 <= 110',
        'x2 <= 80',
        'x1 + x2 <= 150'
    ]
}
```

## 4: Gurobi Code in Python
Now, let's implement this problem using Gurobi in Python:

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, name="cheeseburgers")
    x2 = model.addVar(lb=0, name="fries")

    # Objective function: maximize 1.50*x1 + 1*x2
    model.setObjective(1.50 * x1 + 1 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 <= 110, name="cheeseburger_demand")
    model.addConstr(x2 <= 80, name="fries_demand")
    model.addConstr(x1 + x2 <= 150, name="capacity")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Cheeseburgers: {x1.varValue}")
        print(f"Fries: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```