## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'hamburgers', 'pickles', and 'lemons', which we will denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $8.01x_0 + 3.71x_1 + 4.87x_2$.

## 3: Define the symbolic variables and their corresponding natural language objects
The symbolic variables and their corresponding objects are:
- $x_0$ : hamburgers
- $x_1$ : pickles
- $x_2$ : lemons

## 4: Translate the constraints into symbolic notation
The constraints are:
1. $12x_0 + 7x_2 \geq 17$
2. $19x_1 + 7x_2 \geq 18$
3. $12x_0 + 19x_1 \geq 10$
4. $18x_0 + 17x_1 + 19x_2 \geq 68$
5. $12x_0 + 19x_1 \leq 56$
6. $19x_1 + 7x_2 \leq 53$
7. $12x_0 + 19x_1 + 7x_2 \leq 84$
8. $17x_1 + 19x_2 \leq 110$
9. $18x_0 + 19x_2 \leq 106$
10. $18x_0 + 17x_1 + 19x_2 \leq 71$

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'hamburgers'), ('x1', 'pickles'), ('x2', 'lemons')],
'objective_function': '8.01*x0 + 3.71*x1 + 4.87*x2',
'constraints': [
    '12*x0 + 7*x2 >= 17',
    '19*x1 + 7*x2 >= 18',
    '12*x0 + 19*x1 >= 10',
    '18*x0 + 17*x1 + 19*x2 >= 68',
    '12*x0 + 19*x1 <= 56',
    '19*x1 + 7*x2 <= 53',
    '12*x0 + 19*x1 + 7*x2 <= 84',
    '17*x1 + 19*x2 <= 110',
    '18*x0 + 19*x2 <= 106',
    '18*x0 + 17*x1 + 19*x2 <= 71'
]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobipy as gp

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

# Define the variables
x0 = m.addVar(name="hamburgers", lb=0)
x1 = m.addVar(name="pickles", lb=0)
x2 = m.addVar(name="lemons", lb=0)

# Define the objective function
m.setObjective(8.01*x0 + 3.71*x1 + 4.87*x2, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(12*x0 + 7*x2 >= 17)
m.addConstr(19*x1 + 7*x2 >= 18)
m.addConstr(12*x0 + 19*x1 >= 10)
m.addConstr(18*x0 + 17*x1 + 19*x2 >= 68)
m.addConstr(12*x0 + 19*x1 <= 56)
m.addConstr(19*x1 + 7*x2 <= 53)
m.addConstr(12*x0 + 19*x1 + 7*x2 <= 84)
m.addConstr(17*x1 + 19*x2 <= 110)
m.addConstr(18*x0 + 19*x2 <= 106)
m.addConstr(18*x0 + 17*x1 + 19*x2 <= 71)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Hamburgers: {x0.varValue}")
    print(f"Pickles: {x1.varValue}")
    print(f"Lemons: {x2.varValue}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```