To solve this optimization problem, we first need to understand and translate the given natural language description into a symbolic representation. This involves defining variables for the quantities of apple pies and blueberry pies, formulating the objective function based on these variables, and then listing all constraints in terms of these variables.

Let's define:
- `x1` as the number of apple pies,
- `x2` as the number of blueberry pies.

The objective function is to maximize: `4.17*x1 + 6.08*x2`.

Constraints are:
1. Fiber constraint: `3.95*x1 + 6.48*x2 >= 73` and `3.95*x1 + 6.48*x2 <= 83`.
2. Protein constraint: `12.08*x1 + 1.48*x2 >= 58` and `12.08*x1 + 1.48*x2 <= 172`.
3. Cost constraint: `13.27*x1 + 7.25*x2 >= 56` and `13.27*x1 + 7.25*x2 <= 218`.
4. Iron constraint: `12.13*x1 + 12.43*x2 >= 38` and `12.13*x1 + 12.43*x2 <= 89`.
5. Tastiness rating constraint: `12.81*x1 + 2.93*x2 >= 94` and `12.81*x1 + 2.93*x2 <= 119`.
6. Additional linear constraint: `5*x1 - 4*x2 >= 0`.

All variables are integers since we cannot have a fraction of a pie.

Now, let's represent this problem symbolically:

```json
{
    'sym_variables': [('x1', 'apple pies'), ('x2', 'blueberry pies')],
    'objective_function': 'maximize 4.17*x1 + 6.08*x2',
    'constraints': [
        '3.95*x1 + 6.48*x2 >= 73',
        '3.95*x1 + 6.48*x2 <= 83',
        '12.08*x1 + 1.48*x2 >= 58',
        '12.08*x1 + 1.48*x2 <= 172',
        '13.27*x1 + 7.25*x2 >= 56',
        '13.27*x1 + 7.25*x2 <= 218',
        '12.13*x1 + 12.43*x2 >= 38',
        '12.13*x1 + 12.43*x2 <= 89',
        '12.81*x1 + 2.93*x2 >= 94',
        '12.81*x1 + 2.93*x2 <= 119',
        '5*x1 - 4*x2 >= 0'
    ]
}
```

Here is the Gurobi code for this problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="apple_pies")
x2 = m.addVar(vtype=GRB.INTEGER, name="blueberry_pies")

# Objective function: maximize 4.17*x1 + 6.08*x2
m.setObjective(4.17*x1 + 6.08*x2, GRB.MAXIMIZE)

# Constraints
m.addConstr(3.95*x1 + 6.48*x2 >= 73)
m.addConstr(3.95*x1 + 6.48*x2 <= 83)
m.addConstr(12.08*x1 + 1.48*x2 >= 58)
m.addConstr(12.08*x1 + 1.48*x2 <= 172)
m.addConstr(13.27*x1 + 7.25*x2 >= 56)
m.addConstr(13.27*x1 + 7.25*x2 <= 218)
m.addConstr(12.13*x1 + 12.43*x2 >= 38)
m.addConstr(12.13*x1 + 12.43*x2 <= 89)
m.addConstr(12.81*x1 + 2.93*x2 >= 94)
m.addConstr(12.81*x1 + 2.93*x2 <= 119)
m.addConstr(5*x1 - 4*x2 >= 0)

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of apple pies: {x1.x}")
    print(f"Number of blueberry pies: {x2.x}")
else:
    print("No optimal solution found")
```