## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the amount of green apples in kg,
- $x_2$ as the amount of red apples in kg.

The objective is to minimize the cost, which is $9x_1 + 7x_2$.

## Step 2: Define the constraints based on the problem description

The constraints are:
1. Minimum sugar content: $0.05x_1 + 0.25x_2 \geq 12$,
2. Minimum fiber content: $0.16x_1 + 0.08x_2 \geq 5$,
3. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$.

## Step 3: Symbolic representation in JSON format

```json
{
'sym_variables': [('x1', 'green apples in kg'), ('x2', 'red apples in kg')],
'objective_function': '9*x1 + 7*x2',
'constraints': [
    '0.05*x1 + 0.25*x2 >= 12',
    '0.16*x1 + 0.08*x2 >= 5',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## Step 4: Convert the problem into Gurobi code in Python

```python
import gurobi

def solve_pie_filling_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="green_apples", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="red_apples", lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: minimize cost
    model.setObjective(9 * x1 + 7 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(0.05 * x1 + 0.25 * x2 >= 12, name="min_sugar")
    model.addConstr(0.16 * x1 + 0.08 * x2 >= 5, name="min_fiber")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal amount of green apples: {x1.varValue} kg")
        print(f"Optimal amount of red apples: {x2.varValue} kg")
        print(f"Minimum cost: ${model.objVal:.2f}")
    else:
        print("The problem is infeasible.")

solve_pie_filling_problem()
```