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

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : protein bars
- $x_1$ : cherry pies
- $x_2$ : lemons

## 3: Define the objective function in symbolic notation
The objective function to minimize is $9x_0 + 6x_1 + 8x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $7x_0 \geq 0$ (tastiness rating of protein bars, but this is inherently satisfied)
- $4x_0 \geq 0$ (healthiness rating of protein bars, but this is inherently satisfied)
- $6x_1 \geq 0$ (tastiness rating of cherry pies, but this is inherently satisfied)
- $14x_1 \geq 0$ (healthiness rating of cherry pies, but this is inherently satisfied)
- $11x_2 \geq 0$ (tastiness rating of lemons, but this is inherently satisfied)
- $12x_2 \geq 0$ (healthiness rating of lemons, but this is inherently satisfied)
- $7x_0 + 6x_1 \geq 37$ (total combined tastiness rating from protein bars and cherry pies)
- $6x_1 + 11x_2 \geq 51$ (total combined tastiness rating from cherry pies and lemons)
- $7x_0 + 6x_1 + 11x_2 \geq 52$ (total combined tastiness rating from all)
- $7x_0 + 6x_1 + 11x_2 \geq 52$ (same as above, redundant)
- $14x_1 + 12x_2 \geq 37$ (total combined healthiness rating from cherry pies and lemons)
- $4x_0 + 14x_1 \geq 30$ (total combined healthiness rating from protein bars and cherry pies)
- $4x_0 + 14x_1 + 12x_2 \geq 30$ (total combined healthiness rating from all)
- $-8x_1 + 2x_2 \geq 0$ (constraint on cherry pies and lemons)
- $6x_1 + 11x_2 \leq 163$ (total combined tastiness rating from cherry pies and lemons)
- $7x_0 + 6x_1 + 11x_2 \leq 98$ (total combined tastiness rating from all)
- $4x_0 + 14x_1 \leq 107$ (total combined healthiness rating from protein bars and cherry pies)
- $14x_1 + 12x_2 \leq 64$ (total combined healthiness rating from cherry pies and lemons)

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'protein bars'),
        ('x1', 'cherry pies'),
        ('x2', 'lemons')
    ],
    'objective_function': '9*x0 + 6*x1 + 8*x2',
    'constraints': [
        '7*x0 + 6*x1 >= 37',
        '6*x1 + 11*x2 >= 51',
        '7*x0 + 6*x1 + 11*x2 >= 52',
        '14*x1 + 12*x2 >= 37',
        '4*x0 + 14*x1 >= 30',
        '4*x0 + 14*x1 + 12*x2 >= 30',
        '-8*x1 + 2*x2 >= 0',
        '6*x1 + 11*x2 <= 163',
        '7*x0 + 6*x1 + 11*x2 <= 98',
        '4*x0 + 14*x1 <= 107',
        '14*x1 + 12*x2 <= 64'
    ]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

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

# Set the objective function
m.setObjective(9 * x0 + 6 * x1 + 8 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(7 * x0 + 6 * x1 >= 37)
m.addConstr(6 * x1 + 11 * x2 >= 51)
m.addConstr(7 * x0 + 6 * x1 + 11 * x2 >= 52)
m.addConstr(14 * x1 + 12 * x2 >= 37)
m.addConstr(4 * x0 + 14 * x1 >= 30)
m.addConstr(4 * x0 + 14 * x1 + 12 * x2 >= 30)
m.addConstr(-8 * x1 + 2 * x2 >= 0)
m.addConstr(6 * x1 + 11 * x2 <= 163)
m.addConstr(7 * x0 + 6 * x1 + 11 * x2 <= 98)
m.addConstr(4 * x0 + 14 * x1 <= 107)
m.addConstr(14 * x1 + 12 * x2 <= 64)

# Optimize the model
m.optimize()

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