## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'strips of bacon' and 'oranges'. Let's denote 'strips of bacon' as $x_1$ and 'oranges' as $x_2$. The objective function to maximize is $1.28x_1^2 + 3.91x_2^2 + 8.56x_1 + 5.2x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
1. $8x_1 + 3x_2 \geq 14$ (at least 14 grams of fiber from strips of bacon and oranges),
2. $4x_1 - 6x_2 \geq 0$ (four times the number of strips of bacon, plus minus six times the number of oranges must be at least zero),
3. $8^2x_1^2 + 3^2x_2^2 \leq 15$ (up to 15 grams of fiber from strips of bacon squared plus oranges squared, but this seems to be incorrectly stated as it should likely refer to $x_1^2 + x_2^2$ or another relevant combination; however, based on the problem, it seems there might be confusion - we'll consider $8x_1 + 3x_2 \leq 15$ for the linear case and note $x_1^2 + x_2^2 \leq 15$ as a possible misinterpretation),
4. $8x_1 + 3x_2 \leq 15$ (at most 15 grams of fiber can come from strips of bacon plus oranges),
5. $x_1$ is an integer (an integer number of strips of bacon must be used),
6. $x_2$ can be a non-integer (you are allowed to use a non-integer number of oranges).

## 3: Correctly interpret the fiber constraints
Given the attributes: {'r0': {'description': 'grams of fiber', 'upper_bound': 31, 'x0': 8, 'x1': 3}}, it seems $x_0 = 8$ grams of fiber per strip of bacon and $x_1 = 3$ grams of fiber per orange. The constraints then correctly are:
- $8x_1 + 3x_2 \geq 14$
- $4x_1 - 6x_2 \geq 0$
- $x_1^2 + x_2^2 \leq 15$ seems not directly supported by the problem statement; instead, we focus on $8x_1 + 3x_2 \leq 15$.

## 4: Formulate the symbolic representation
The symbolic representation is:
- Variables: $x_1$ for 'strips of bacon', $x_2$ for 'oranges'
- Objective function: $1.28x_1^2 + 3.91x_2^2 + 8.56x_1 + 5.2x_2$
- Constraints:
  1. $8x_1 + 3x_2 \geq 14$
  2. $4x_1 - 6x_2 \geq 0$
  3. $8x_1 + 3x_2 \leq 15$
  4. $x_1$ is an integer.

## 5: Convert to Gurobi code
```python
import gurobi

# Define the model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="strips_of_bacon", vtype=gurobi.GRB.INTEGER)
x2 = model.addVar(name="oranges")

# Objective function
model.setObjective(1.28*x1**2 + 3.91*x2**2 + 8.56*x1 + 5.2*x2, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(8*x1 + 3*x2 >= 14, name="fiber_min")
model.addConstr(4*x1 - 6*x2 >= 0, name="bacon_orange_ratio")
model.addConstr(8*x1 + 3*x2 <= 15, name="fiber_max")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Strips of bacon: ", x1.varValue)
    print("Oranges: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 6: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'strips of bacon'), ('x2', 'oranges')],
    'objective_function': '1.28*x1^2 + 3.91*x2^2 + 8.56*x1 + 5.2*x2',
    'constraints': [
        '8*x1 + 3*x2 >= 14',
        '4*x1 - 6*x2 >= 0',
        '8*x1 + 3*x2 <= 15'
    ]
}
```