To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves identifying variables and constraints based on the provided information.

### Symbolic Representation:

- Let `x1` represent "milligrams of potassium".
- Let `x2` represent "milligrams of vitamin A".

The objective function to minimize is: `5*x1 + 3*x2`.

Constraints are as follows:
1. Kidney support index constraint: `17*x1 + 5*x2 >= 49`.
2. Energy stability index constraint: `21*x1 + 23*x2 >= 120`.
3. Immune support index constraint: `24*x1 + 24*x2 >= 95`.
4. Muscle growth index constraint: `21*x1 + 6*x2 >= 161`.
5. Cognitive performance index constraint: `3*x1 + 17*x2 >= 86`.
6. Additional constraint: `3*x1 - 9*x2 >= 0`.
7. Upper bound kidney support index: `17*x1 + 5*x2 <= 263`.
8. Upper bound energy stability index: `21*x1 + 23*x2 <= 199`.
9. Upper bound immune support index: `24*x1 + 24*x2 <= 128`.
10. Upper bound muscle growth index: `21*x1 + 6*x2 <= 181`.
11. Upper bound cognitive performance index: `3*x1 + 17*x2 <= 207`.

### Gurobi Code:

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_potassium")
x2 = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_A")

# Set the objective function
m.setObjective(5*x1 + 3*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(17*x1 + 5*x2 >= 49, "kidney_support_index_min")
m.addConstr(21*x1 + 23*x2 >= 120, "energy_stability_index_min")
m.addConstr(24*x1 + 24*x2 >= 95, "immune_support_index_min")
m.addConstr(21*x1 + 6*x2 >= 161, "muscle_growth_index_min")
m.addConstr(3*x1 + 17*x2 >= 86, "cognitive_performance_index_min")
m.addConstr(3*x1 - 9*x2 >= 0, "additional_constraint")
m.addConstr(17*x1 + 5*x2 <= 263, "kidney_support_index_max")
m.addConstr(21*x1 + 23*x2 <= 199, "energy_stability_index_max")
m.addConstr(24*x1 + 24*x2 <= 128, "immune_support_index_max")
m.addConstr(21*x1 + 6*x2 <= 181, "muscle_growth_index_max")
m.addConstr(3*x1 + 17*x2 <= 207, "cognitive_performance_index_max")

# Optimize model
m.optimize()

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

```json
{
  'sym_variables': [('x1', 'milligrams_of_potassium'), ('x2', 'milligrams_of_vitamin_A')],
  'objective_function': '5*x1 + 3*x2',
  'constraints': [
    '17*x1 + 5*x2 >= 49',
    '21*x1 + 23*x2 >= 120',
    '24*x1 + 24*x2 >= 95',
    '21*x1 + 6*x2 >= 161',
    '3*x1 + 17*x2 >= 86',
    '3*x1 - 9*x2 >= 0',
    '17*x1 + 5*x2 <= 263',
    '21*x1 + 23*x2 <= 199',
    '24*x1 + 24*x2 <= 128',
    '21*x1 + 6*x2 <= 181',
    '3*x1 + 17*x2 <= 207'
  ]
}
```