## Step 1: Define the symbolic representation of the variables
The variables are 'kiwis', 'chicken breasts', and 'granola bars', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $1.28x_1^2 + 7.27x_1x_3 + 7.84x_2^2 + 7.73x_1 + 9.52x_2 + 2.5x_3$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $1.05x_1 \geq 0$ (tastiness rating of kiwis, but this is always true given $x_1 \geq 0$)
- $17.17x_2 \geq 0$ (tastiness rating of chicken breasts, but this is always true given $x_2 \geq 0$)
- $13.54x_3 \geq 0$ (tastiness rating of granola bars, but this is always true given $x_3 \geq 0$)
- $1.05x_1 + 17.17x_2 \geq 54$
- $17.17x_2 + 13.54x_3 \geq 38$
- $17.17x_2 + 13.54x_3 \leq 170$
- $1.05x_1 + 17.17x_2 + 13.54x_3 \leq 170$

## 4: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'kiwis'), ('x2', 'chicken breasts'), ('x3', 'granola bars')],
'objective_function': '1.28*x1^2 + 7.27*x1*x3 + 7.84*x2^2 + 7.73*x1 + 9.52*x2 + 2.5*x3',
'constraints': [
    '1.05*x1 + 17.17*x2 >= 54',
    '17.17*x2 + 13.54*x3 >= 38',
    '17.17*x2 + 13.54*x3 <= 170',
    '1.05*x1 + 17.17*x2 + 13.54*x3 <= 170'
]
}
```

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

# Define the model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="kiwis", lb=0)  # kiwis
x2 = m.addVar(name="chicken breasts", lb=0)  # chicken breasts
x3 = m.addVar(name="granola bars", lb=0)  # granola bars

# Define the objective function
m.setObjective(1.28*x1**2 + 7.27*x1*x3 + 7.84*x2**2 + 7.73*x1 + 9.52*x2 + 2.5*x3, gp.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(1.05*x1 + 17.17*x2 >= 54)
m.addConstr(17.17*x2 + 13.54*x3 >= 38)
m.addConstr(17.17*x2 + 13.54*x3 <= 170)
m.addConstr(1.05*x1 + 17.17*x2 + 13.54*x3 <= 170)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"kiwis: {x1.varValue}")
    print(f"chicken breasts: {x2.varValue}")
    print(f"granola bars: {x3.varValue}")
    print(f"Objective: {m.objVal}")
else:
    print("No optimal solution found.")
```