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.

The symbolic representation of the problem is as follows:

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

This representation defines two variables, `x1` for the number of cheeseburgers and `x2` for the number of fries. The objective function aims to maximize profit, which is calculated as $1.50 per cheeseburger and $1 per fry. The constraints ensure that the production of both items is non-negative (`x1 >= 0`, `x2 >= 0`), does not exceed daily demand (`x1 <= 110`, `x2 <= 80`), and does not surpass the total capacity of 150 items (`x1 + x2 <= 150`).

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

```python
from gurobipy import *

# Create a model
m = Model("Fast_Food_Optimization")

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="cheeseburgers")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="fries")

# Set the objective function
m.setObjective(1.50*x1 + 1*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 <= 110, "daily_demand_cheeseburgers")
m.addConstr(x2 <= 80, "daily_demand_fries")
m.addConstr(x1 + x2 <= 150, "total_capacity")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Cheeseburgers to produce: {x1.x}")
    print(f"Fries to produce: {x2.x}")
    print(f"Maximum profit: ${m.objVal:.2f}")
else:
    print("No optimal solution found")
```