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

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $7x_0^2 + 7x_0x_1 + 9x_0x_2 + 2x_1^2 + 7x_1x_2 + 7x_2^2 + 8x_2$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $4x_0 + 8x_1 + 23x_2 \geq 43$ (total protein constraint)
- $3x_0 + 4x_2 \geq 79$ (carbohydrates from granola bars and milkshakes)
- $6^2x_1^2 + 4^2x_2^2 \geq 68$ (carbohydrates from protein bars squared and milkshakes squared)
- $4^2x_0^2 + 23^2x_2^2 \leq 176$ (protein from granola bars squared and milkshakes squared)
- $4^2x_0^2 + 8^2x_1^2 \leq 73$ (protein from granola bars squared and protein bars squared)
- $4x_0 + 8x_1 + 23x_2 \leq 73$ (total protein)
- $6x_1 + 4x_2 \leq 85$ (carbohydrates from protein bars and milkshakes)
- $3x_0 + 4x_2 \leq 164$ (carbohydrates from granola bars and milkshakes)
- $3x_0 + 6x_1 + 4x_2 \leq 164$ (total carbohydrates)

## 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'granola bars'), ('x1', 'protein bars'), ('x2', 'milkshakes')],
    'objective_function': '7*x0^2 + 7*x0*x1 + 9*x0*x2 + 2*x1^2 + 7*x1*x2 + 7*x2^2 + 8*x2',
    'constraints': [
        '4*x0 + 8*x1 + 23*x2 >= 43',
        '3*x0 + 4*x2 >= 79',
        '36*x1^2 + 16*x2^2 >= 68',
        '16*x0^2 + 529*x2^2 <= 176',
        '16*x0^2 + 64*x1^2 <= 73',
        '4*x0 + 8*x1 + 23*x2 <= 73',
        '6*x1 + 4*x2 <= 85',
        '3*x0 + 4*x2 <= 164',
        '3*x0 + 6*x1 + 4*x2 <= 164'
    ]
}
```

## Step 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

# Define the variables
x0 = model.addVar(name="granola_bars", lb=0)
x1 = model.addVar(name="protein_bars", lb=0)
x2 = model.addVar(name="milkshakes", lb=0)

# Define the objective function
model.setObjective(7*x0**2 + 7*x0*x1 + 9*x0*x2 + 2*x1**2 + 7*x1*x2 + 7*x2**2 + 8*x2, gurobi.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(4*x0 + 8*x1 + 23*x2 >= 43)
model.addConstr(3*x0 + 4*x2 >= 79)
model.addConstr(36*x1**2 + 16*x2**2 >= 68)
model.addConstr(16*x0**2 + 529*x2**2 <= 176)
model.addConstr(16*x0**2 + 64*x1**2 <= 73)
model.addConstr(4*x0 + 8*x1 + 23*x2 <= 73)
model.addConstr(6*x1 + 4*x2 <= 85)
model.addConstr(3*x0 + 4*x2 <= 164)
model.addConstr(3*x0 + 6*x1 + 4*x2 <= 164)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Granola bars: ", x0.x)
    print("Protein bars: ", x1.x)
    print("Milkshakes: ", x2.x)
else:
    print("The model is infeasible")
```