To solve the given problem, we first need to translate the natural language description into a symbolic representation. Let's denote the number of servings of the orange flavored drink as $x_1$ and the number of servings of the apple flavored drink as $x_2$. The objective is to minimize the total cost, which can be represented as $8x_1 + 5x_2$. 

The constraints are:
- The minimum requirement for fiber: $4x_1 + 6x_2 \geq 13$
- The minimum requirement for iron: $5x_1 + 3x_2 \geq 13$
- Non-negativity constraints since we cannot have negative servings of drinks: $x_1 \geq 0, x_2 \geq 0$

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of servings of orange flavored drink'), ('x2', 'number of servings of apple flavored drink')],
    'objective_function': '8*x1 + 5*x2',
    'constraints': ['4*x1 + 6*x2 >= 13', '5*x1 + 3*x2 >= 13', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code in Python to solve this linear programming problem:

```python
from gurobipy import *

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

# Create variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="orange_flavored_drink")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="apple_flavored_drink")

# Set the objective function
m.setObjective(8*x1 + 5*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(4*x1 + 6*x2 >= 13, "fiber_requirement")
m.addConstr(5*x1 + 3*x2 >= 13, "iron_requirement")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of servings of orange flavored drink: {x1.x}")
    print(f"Number of servings of apple flavored drink: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found")
```