## Step 1: Define the symbolic representation of the variables
The variables are 'oreos', 'cheeseburgers', and 'lemons', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $8x_0 + 9x_1 + 9x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $9x_0 + 19x_2 \geq 23$ (total combined sourness index from oreos and lemons)
- $7x_1 + x_2 \geq 15$ (at least 15 grams of fiber from cheeseburgers and lemons)
- $7x_1 + 19x_2 \leq 79$ (total combined sourness index from cheeseburgers and lemons)
- $9x_0 + 7x_1 \leq 120$ (total combined sourness index from oreos and cheeseburgers)
- $9x_0 + 7x_1 + 19x_2 \leq 120$ (total combined sourness index from all)
- $x_0 + 7x_1 \leq 95$ (at most 95 grams of fiber from oreos and cheeseburgers)
- $7x_1 + x_2 \leq 35$ (at most 35 grams of fiber from cheeseburgers and lemons)
- $x_0 + 7x_1 + x_2 \leq 35$ (at most 35 grams of fiber from all)

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'oreos'), ('x1', 'cheeseburgers'), ('x2', 'lemons')],
    'objective_function': '8*x0 + 9*x1 + 9*x2',
    'constraints': [
        '9*x0 + 19*x2 >= 23',
        '7*x1 + x2 >= 15',
        '7*x1 + 19*x2 <= 79',
        '9*x0 + 7*x1 <= 120',
        '9*x0 + 7*x1 + 19*x2 <= 120',
        'x0 + 7*x1 <= 95',
        '7*x1 + x2 <= 35',
        'x0 + 7*x1 + x2 <= 35'
    ]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="oreos", lb=0)
x1 = model.addVar(name="cheeseburgers", lb=0)
x2 = model.addVar(name="lemons", lb=0)

# Define the objective function
model.setObjective(8*x0 + 9*x1 + 9*x2, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(9*x0 + 19*x2 >= 23, name="sourness_oreos_lemons")
model.addConstr(7*x1 + x2 >= 15, name="fiber_cheeseburgers_lemons")
model.addConstr(7*x1 + 19*x2 <= 79, name="sourness_cheeseburgers_lemons")
model.addConstr(9*x0 + 7*x1 <= 120, name="sourness_oreos_cheeseburgers")
model.addConstr(9*x0 + 7*x1 + 19*x2 <= 120, name="sourness_all")
model.addConstr(x0 + 7*x1 <= 95, name="fiber_oreos_cheeseburgers")
model.addConstr(7*x1 + x2 <= 35, name="fiber_cheeseburgers_lemons")
model.addConstr(x0 + 7*x1 + x2 <= 35, name="fiber_all")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Oreos: ", x0.varValue)
    print("Cheeseburgers: ", x1.varValue)
    print("Lemons: ", x2.varValue)
else:
    print("The model is infeasible")
```