## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_1$ represents 'milligrams of vitamin A'
- $x_2$ represents 'milligrams of vitamin B6'
- $x_3$ represents 'milligrams of zinc'
- $x_4$ represents 'milligrams of vitamin B1'
- $x_5$ represents 'milligrams of vitamin D'

The objective function to maximize is: $2x_1 + 7x_2 + 5x_3 + 7x_4 + 3x_5$

## Step 2: List the constraints
Constraints based on the kidney support index:
- $4x_1 \leq 253$
- $4x_2 \leq 253$
- $1x_3 \leq 253$
- $6x_4 \leq 253$
- $6x_5 \leq 253$
- $6x_4 + 6x_5 \geq 17$
- $1x_3 + 6x_5 \geq 24$
- $1x_3 + 6x_4 \geq 34$
- $4x_2 + 1x_3 + 6x_5 \geq 31$
- $4x_1 + 4x_2 + 6x_4 \geq 31$
- $4x_1 + 1x_3 + 6x_4 \geq 31$
- $4x_2 + 6x_4 + 6x_5 \geq 31$
- $4x_1 + 1x_3 + 6x_5 \geq 31$
- $4x_1 + 4x_2 + 6x_5 \geq 31$
- $1x_3 + 6x_4 + 6x_5 \geq 31$
- $4x_2 + 1x_3 + 6x_5 \geq 33$
- $4x_1 + 4x_2 + 6x_4 \geq 33$
- $4x_1 + 1x_3 + 6x_4 \geq 33$
- $4x_2 + 6x_4 + 6x_5 \geq 33$
- $4x_1 + 1x_3 + 6x_5 \geq 33$
- $4x_1 + 4x_2 + 6x_5 \geq 33$
- $1x_3 + 6x_4 + 6x_5 \geq 33$
- $4x_2 + 1x_3 + 6x_5 \geq 48$
- $4x_1 + 4x_2 + 6x_4 \geq 48$
- $4x_1 + 1x_3 + 6x_4 \geq 48$
- $4x_2 + 6x_4 + 6x_5 \geq 48$
- $4x_1 + 1x_3 + 6x_5 \geq 48$
- $4x_1 + 4x_2 + 6x_5 \geq 48$
- $1x_3 + 6x_4 + 6x_5 \geq 48$
- $4x_2 + 1x_3 \leq 221$
- $4x_2 + 6x_5 \leq 55$
- $1x_3 + 6x_4 \leq 229$
- $6x_4 + 6x_5 \leq 98$
- $4x_1 + 4x_2 + 6x_4 \leq 161$
- $4x_1 + 1x_3 + 6x_4 \leq 95$
- $4x_1 + 4x_2 + 1x_3 \leq 244$
- $4x_1 + 4x_2 + 1x_3 + 6x_4 + 6x_5 \leq 244$

## Step 3: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin A'),
        ('x2', 'milligrams of vitamin B6'),
        ('x3', 'milligrams of zinc'),
        ('x4', 'milligrams of vitamin B1'),
        ('x5', 'milligrams of vitamin D')
    ],
    'objective_function': '2*x1 + 7*x2 + 5*x3 + 7*x4 + 3*x5',
    'constraints': [
        '4*x1 <= 253',
        '4*x2 <= 253',
        '1*x3 <= 253',
        '6*x4 <= 253',
        '6*x5 <= 253',
        '6*x4 + 6*x5 >= 17',
        '1*x3 + 6*x5 >= 24',
        '1*x3 + 6*x4 >= 34',
        '4*x2 + 1*x3 + 6*x5 >= 31',
        '4*x1 + 4*x2 + 6*x4 >= 31',
        '4*x1 + 1*x3 + 6*x4 >= 31',
        '4*x2 + 6*x4 + 6*x5 >= 31',
        '4*x1 + 1*x3 + 6*x5 >= 31',
        '4*x1 + 4*x2 + 6*x5 >= 31',
        '1*x3 + 6*x4 + 6*x5 >= 31',
        '4*x2 + 1*x3 + 6*x5 >= 33',
        '4*x1 + 4*x2 + 6*x4 >= 33',
        '4*x1 + 1*x3 + 6*x4 >= 33',
        '4*x2 + 6*x4 + 6*x5 >= 33',
        '4*x1 + 1*x3 + 6*x5 >= 33',
        '4*x1 + 4*x2 + 6*x5 >= 33',
        '1*x3 + 6*x4 + 6*x5 >= 33',
        '4*x2 + 1*x3 + 6*x5 >= 48',
        '4*x1 + 4*x2 + 6*x4 >= 48',
        '4*x1 + 1*x3 + 6*x4 >= 48',
        '4*x2 + 6*x4 + 6*x5 >= 48',
        '4*x1 + 1*x3 + 6*x5 >= 48',
        '4*x1 + 4*x2 + 6*x5 >= 48',
        '1*x3 + 6*x4 + 6*x5 >= 48',
        '4*x2 + 1*x3 <= 221',
        '4*x2 + 6*x5 <= 55',
        '1*x3 + 6*x4 <= 229',
        '6*x4 + 6*x5 <= 98',
        '4*x1 + 4*x2 + 6*x4 <= 161',
        '4*x1 + 1*x3 + 6*x4 <= 95',
        '4*x1 + 4*x2 + 1*x3 <= 244',
        '4*x1 + 4*x2 + 1*x3 + 6*x4 + 6*x5 <= 244'
    ]
}
```

## Step 4: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin A
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B6
    x3 = model.addVar(name="x3", lb=0)  # milligrams of zinc
    x4 = model.addVar(name="x4", lb=0)  # milligrams of vitamin B1
    x5 = model.addVar(name="x5", lb=0)  # milligrams of vitamin D

    # Objective function
    model.setObjective(2*x1 + 7*x2 + 5*x3 + 7*x4 + 3*x5, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4*x1 <= 253)
    model.addConstr(4*x2 <= 253)
    model.addConstr(x3 <= 253)
    model.addConstr(6*x4 <= 253)
    model.addConstr(6*x5 <= 253)
    model.addConstr(6*x4 + 6*x5 >= 17)
    model.addConstr(x3 + 6*x5 >= 24)
    model.addConstr(x3 + 6*x4 >= 34)
    model.addConstr(4*x2 + x3 + 6*x5 >= 31)
    model.addConstr(4*x1 + 4*x2 + 6*x4 >= 31)
    model.addConstr(4*x1 + x3 + 6*x4 >= 31)
    model.addConstr(4*x2 + 6*x4 + 6*x5 >= 31)
    model.addConstr(4*x1 + x3 + 6*x5 >= 31)
    model.addConstr(4*x1 + 4*x2 + 6*x5 >= 31)
    model.addConstr(x3 + 6*x4 + 6*x5 >= 31)
    model.addConstr(4*x2 + x3 + 6*x5 >= 33)
    model.addConstr(4*x1 + 4*x2 + 6*x4 >= 33)
    model.addConstr(4*x1 + x3 + 6*x4 >= 33)
    model.addConstr(4*x2 + 6*x4 + 6*x5 >= 33)
    model.addConstr(4*x1 + x3 + 6*x5 >= 33)
    model.addConstr(4*x1 + 4*x2 + 6*x5 >= 33)
    model.addConstr(x3 + 6*x4 + 6*x5 >= 33)
    model.addConstr(4*x2 + x3 + 6*x5 >= 48)
    model.addConstr(4*x1 + 4*x2 + 6*x4 >= 48)
    model.addConstr(4*x1 + x3 + 6*x4 >= 48)
    model.addConstr(4*x2 + 6*x4 + 6*x5 >= 48)
    model.addConstr(4*x1 + x3 + 6*x5 >= 48)
    model.addConstr(4*x1 + 4*x2 + 6*x5 >= 48)
    model.addConstr(x3 + 6*x4 + 6*x5 >= 48)
    model.addConstr(4*x2 + x3 <= 221)
    model.addConstr(4*x2 + 6*x5 <= 55)
    model.addConstr(x3 + 6*x4 <= 229)
    model.addConstr(6*x4 + 6*x5 <= 98)
    model.addConstr(4*x1 + 4*x2 + 6*x4 <= 161)
    model.addConstr(4*x1 + x3 + 6*x4 <= 95)
    model.addConstr(4*x1 + 4*x2 + x3 <= 244)
    model.addConstr(4*x1 + 4*x2 + x3 + 6*x4 + 6*x5 <= 244)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin A: {x1.varValue}")
        print(f"Milligrams of vitamin B6: {x2.varValue}")
        print(f"Milligrams of zinc: {x3.varValue}")
        print(f"Milligrams of vitamin B1: {x4.varValue}")
        print(f"Milligrams of vitamin D: {x5.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```