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

Let's denote:
- $x_1$ as the milligrams of vitamin B2,
- $x_2$ as the milligrams of iron.

The objective function to maximize is: $2x_1 + 2x_2$

Constraints:
1. Kidney support index for vitamin B2: $24x_1$
2. Cardiovascular support index for vitamin B2: $20x_1$
3. Kidney support index for iron: $19x_2$
4. Cardiovascular support index for iron: $10x_2$
5. Total combined kidney support index: $24x_1 + 19x_2 \geq 63$
6. Total combined cardiovascular support index: $20x_1 + 10x_2 \geq 107$
7. Linear inequality constraint: $-8x_1 + 6x_2 \geq 0$
8. Maximum total combined kidney support index: $24x_1 + 19x_2 \leq 238$
9. Maximum total combined cardiovascular support index: $20x_1 + 10x_2 \leq 254$

Given that $x_1$ can be fractional but $x_2$ must be an integer, we have a mixed-integer linear programming (MILP) problem.

Here is the symbolic representation of the problem:

```json
{
  'sym_variables': [('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of iron')],
  'objective_function': '2*x1 + 2*x2',
  'constraints': [
    '24*x1 + 19*x2 >= 63',
    '20*x1 + 10*x2 >= 107',
    '-8*x1 + 6*x2 >= 0',
    '24*x1 + 19*x2 <= 238',
    '20*x1 + 10*x2 <= 254'
  ]
}
```

To solve this problem using Gurobi, we'll write the Python code as follows:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B2")
x2 = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_iron")

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

# Add constraints
m.addConstr(24*x1 + 19*x2 >= 63, name="kidney_support_index_min")
m.addConstr(20*x1 + 10*x2 >= 107, name="cardiovascular_support_index_min")
m.addConstr(-8*x1 + 6*x2 >= 0, name="linear_inequality_constraint")
m.addConstr(24*x1 + 19*x2 <= 238, name="kidney_support_index_max")
m.addConstr(20*x1 + 10*x2 <= 254, name="cardiovascular_support_index_max")

# Optimize the model
m.optimize()

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