## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'apple pies' and 'blueberry pies', which we can denote as $x_0$ and $x_1$ respectively. The objective function to maximize is $4.17x_0 + 6.08x_1$. The constraints are as follows:
- $3.95x_0 + 6.48x_1 \geq 73$ (fiber constraint)
- $12.08x_0 + 1.48x_1 \geq 58$ (protein constraint)
- $13.27x_0 + 7.25x_1 \geq 56$ (cost constraint)
- $12.13x_0 + 12.43x_1 \geq 38$ (iron constraint)
- $12.81x_0 + 2.93x_1 \geq 94$ (tastiness rating constraint)
- $5x_0 - 4x_1 \geq 0$ (pie ratio constraint)
- $3.95x_0 + 6.48x_1 \leq 83$ (fiber upper bound constraint)
- $12.08x_0 + 1.48x_1 \leq 172$ (protein upper bound constraint)
- $13.27x_0 + 7.25x_1 \leq 218$ (cost upper bound constraint)
- $12.13x_0 + 12.43x_1 \leq 89$ (iron upper bound constraint)
- $12.81x_0 + 2.93x_1 \leq 119$ (tastiness rating upper bound constraint)

## Step 2: Convert the problem into a Gurobi-compatible format
We need to define the variables, the objective function, and the constraints in a format that Gurobi can understand.

## 3: Write the Gurobi code
```python
import gurobi

# Define the model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="apple_pies", vtype=gurobi.GRB.INTEGER)  # apple pies
x1 = model.addVar(name="blueberry_pies", vtype=gurobi.GRB.INTEGER)  # blueberry pies

# Define the objective function
model.setObjective(4.17 * x0 + 6.08 * x1, gurobi.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(3.95 * x0 + 6.48 * x1 >= 73, name="fiber_constraint")
model.addConstr(12.08 * x0 + 1.48 * x1 >= 58, name="protein_constraint")
model.addConstr(13.27 * x0 + 7.25 * x1 >= 56, name="cost_constraint")
model.addConstr(12.13 * x0 + 12.43 * x1 >= 38, name="iron_constraint")
model.addConstr(12.81 * x0 + 2.93 * x1 >= 94, name="tastiness_rating_constraint")
model.addConstr(5 * x0 - 4 * x1 >= 0, name="pie_ratio_constraint")
model.addConstr(3.95 * x0 + 6.48 * x1 <= 83, name="fiber_upper_bound_constraint")
model.addConstr(12.08 * x0 + 1.48 * x1 <= 172, name="protein_upper_bound_constraint")
model.addConstr(13.27 * x0 + 7.25 * x1 <= 218, name="cost_upper_bound_constraint")
model.addConstr(12.13 * x0 + 12.43 * x1 <= 89, name="iron_upper_bound_constraint")
model.addConstr(12.81 * x0 + 2.93 * x1 <= 119, name="tastiness_rating_upper_bound_constraint")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objval)
    print("Apple pies: ", x0.varValue)
    print("Blueberry pies: ", x1.varValue)
else:
    print("The model is infeasible")
```

## 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'apple pies'), ('x1', 'blueberry pies')],
    'objective_function': '4.17*x0 + 6.08*x1',
    'constraints': [
        '3.95*x0 + 6.48*x1 >= 73',
        '12.08*x0 + 1.48*x1 >= 58',
        '13.27*x0 + 7.25*x1 >= 56',
        '12.13*x0 + 12.43*x1 >= 38',
        '12.81*x0 + 2.93*x1 >= 94',
        '5*x0 - 4*x1 >= 0',
        '3.95*x0 + 6.48*x1 <= 83',
        '12.08*x0 + 1.48*x1 <= 172',
        '13.27*x0 + 7.25*x1 <= 218',
        '12.13*x0 + 12.43*x1 <= 89',
        '12.81*x0 + 2.93*x1 <= 119'
    ]
}
```