To tackle this problem, we first need to translate the natural language description into a symbolic representation of the optimization problem. This involves defining the variables, the objective function, and the constraints using mathematical notation.

Let's denote:
- \(x_1\) as the quantity of kiwis,
- \(x_2\) as the number of chicken breasts,
- \(x_3\) as the amount of granola bars.

The objective function to maximize is given by:
\[1.28x_1^2 + 7.27x_1x_3 + 7.84x_2^2 + 7.73x_1 + 9.52x_2 + 2.5x_3\]

The constraints based on the tastiness ratings are:
1. \(1.05x_1 + 17.17x_2 \geq 54\)
2. \(17.17x_2 + 13.54x_3 \geq 38\)
3. \(17.17x_2 + 13.54x_3 \leq 170\)
4. \(1.05x_1 + 17.17x_2 + 13.54x_3 \leq 170\)

All variables \(x_1, x_2, x_3\) can be fractional.

The symbolic representation of the problem is:
```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'
    ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(name="kiwis", lb=-GRB.INFINITY)  # Allows for fractional values
x2 = m.addVar(name="chicken_breasts", lb=-GRB.INFINITY)
x3 = m.addVar(name="granola_bars", lb=-GRB.INFINITY)

# Set 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, GRB.MAXIMIZE)

# Add constraints
m.addConstr(1.05*x1 + 17.17*x2 >= 54, name="tastiness_kiwis_chicken")
m.addConstr(17.17*x2 + 13.54*x3 >= 38, name="tastiness_chicken_granola")
m.addConstr(17.17*x2 + 13.54*x3 <= 170, name="max_tastiness_chicken_granola")
m.addConstr(1.05*x1 + 17.17*x2 + 13.54*x3 <= 170, name="total_max_tastiness")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    for v in m.getVars():
        print(f"{v.varName}: {v.x}")
else:
    print("No optimal solution found")
```