Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x1`: Number of cheeseburgers produced daily
* `x2`: Number of fries produced daily

**Objective Function:**

Maximize profit: `1.5 * x1 + 1 * x2`

**Constraints:**

* **Demand Constraints:**
    * `x1 <= 110` (Cheeseburger demand)
    * `x2 <= 80`  (Fries demand)
* **Capacity Constraint:**
    * `x1 + x2 <= 150` (Total production capacity)
* **Non-negativity Constraints:**
    * `x1 >= 0`
    * `x2 >= 0`


```python
import gurobipy as gp
from gurobipy import GRB

# Create a new model
model = gp.Model("FastFoodOptimization")

# Create decision variables
x1 = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="cheeseburgers")
x2 = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="fries")

# Set objective function
model.setObjective(1.5 * x1 + x2, GRB.MAXIMIZE)

# Add constraints
model.addConstr(x1 <= 110, "demand_cheeseburgers")
model.addConstr(x2 <= 80, "demand_fries")
model.addConstr(x1 + x2 <= 150, "capacity")

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print(f"Optimal Profit: ${model.objVal}")
    print(f"Number of Cheeseburgers: {x1.x}")
    print(f"Number of Fries: {x2.x}")
elif model.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status: {model.status}")

```
