To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the problem statement.

### Symbolic Representation:

- **Variables:**
  - Let \(x_1\) represent 'milligrams of vitamin B2'.
  - Let \(x_2\) represent 'grams of fat'.

- **Objective Function:**
  The objective is to minimize \(3x_1 + 2x_2\).

- **Constraints:**
  1. Kidney support index for milligrams of vitamin B2: \(13x_1\)
  2. Cardiovascular support index for milligrams of vitamin B2: \(8x_1\)
  3. Kidney support index for grams of fat: \(13x_2\)
  4. Cardiovascular support index for grams of fat: \(3x_2\)
  5. Total combined kidney support index \(\geq 52\): \(13x_1 + 13x_2 \geq 52\)
  6. Same as constraint 5, emphasizing the minimum requirement.
  7. Total combined cardiovascular support index \(\geq 32\): \(8x_1 + 3x_2 \geq 32\)
  8. Same as constraint 7, reiterating the minimum requirement.
  9. \(-3x_1 + 7x_2 \geq 0\)
  10. Maximum total combined kidney support index: \(13x_1 + 13x_2 \leq 125\)
  11. Maximum total combined cardiovascular support index: \(8x_1 + 3x_2 \leq 55\)

Given the variables are non-integer (continuous), we can represent this problem symbolically as follows:

```json
{
  'sym_variables': [('x1', 'milligrams of vitamin B2'), ('x2', 'grams of fat')],
  'objective_function': '3*x1 + 2*x2',
  'constraints': [
    '13*x1 + 13*x2 >= 52',
    '8*x1 + 3*x2 >= 32',
    '-3*x1 + 7*x2 >= 0',
    '13*x1 + 13*x2 <= 125',
    '8*x1 + 3*x2 <= 55'
  ]
}
```

### Gurobi Code:

To solve this optimization problem using Gurobi, we will write the Python code as follows. This code defines the model, adds variables, sets the objective function, and adds constraints according to the symbolic representation.

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(lb=0, name="milligrams_of_vitamin_B2")
x2 = m.addVar(lb=0, name="grams_of_fat")

# Set the objective function
m.setObjective(3*x1 + 2*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(13*x1 + 13*x2 >= 52, "kidney_support_index_min")
m.addConstr(8*x1 + 3*x2 >= 32, "cardiovascular_support_index_min")
m.addConstr(-3*x1 + 7*x2 >= 0, "additional_constraint")
m.addConstr(13*x1 + 13*x2 <= 125, "kidney_support_index_max")
m.addConstr(8*x1 + 3*x2 <= 55, "cardiovascular_support_index_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B2: {x1.x}")
    print(f"Grams of Fat: {x2.x}")
else:
    print("No optimal solution found")
```