## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of potassium', 'milligrams of vitamin B3', 'milligrams of zinc']. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to minimize is $8.92x_1 + 6.55x_2 + 4.0x_3$.

## Step 3: List all the constraints
The constraints are:
- $15x_1 \leq 93$
- $7x_1 \leq 146$
- $7x_1 \leq 146$
- $8x_1 \leq 253$
- $1x_1 \leq 110$
- $2x_2 \leq 93$
- $5x_2 \leq 146$
- $9x_2 \leq 146$
- $10x_2 \leq 253$
- $12x_2 \leq 110$
- $13x_3 \leq 93$
- $10x_3 \leq 146$
- $16x_3 \leq 146$
- $17x_3 \leq 253$
- $10x_3 \leq 110$
- $15x_1 + 13x_3 \geq 10$
- $2x_2 + 13x_3 \geq 27$
- $15x_1 + 2x_2 + 13x_3 \geq 19$
- $15x_1 + 2x_2 + 13x_3 \geq 19$
- $5x_2 + 10x_3 \geq 43$
- $7x_1 + 10x_3 \geq 24$
- $7x_1 + 5x_2 \geq 17$
- $7x_1 + 5x_2 + 10x_3 \geq 17$
- $7x_1 + 16x_3 \geq 42$
- $7x_1 + 9x_2 \geq 30$
- $9x_2 + 16x_3 \geq 25$
- $7x_1 + 9x_2 + 16x_3 \geq 36$
- $7x_1 + 9x_2 + 16x_3 \geq 36$
- $10x_2 + 17x_3 \geq 64$
- $8x_1 + 10x_2 \geq 70$
- $8x_1 + 17x_3 \geq 56$
- $8x_1 + 10x_2 + 17x_3 \geq 56$
- $1x_1 + 10x_3 \geq 36$
- $1x_1 + 12x_2 \geq 29$
- $1x_1 + 12x_2 + 10x_3 \geq 29$
- $4x_1 - 7x_2 \geq 0$
- $15x_1 + 13x_3 \leq 58$
- $15x_1 + 2x_2 \leq 88$
- $8x_1 + 17x_3 \leq 147$
- $8x_1 + 10x_2 \leq 213$

## 4: Determine the variable types
- $x_1$ can be fractional (milligrams of potassium)
- $x_2$ must be an integer (milligrams of vitamin B3)
- $x_3$ can be fractional (milligrams of zinc)

## 5: Formulate the problem in Gurobi
We will use Gurobi's Python API to formulate and solve this problem.

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="milligrams_of_potassium", lb=0, ub=None)
x2 = model.addVar(name="milligrams_of_vitamin_B3", lb=0, type=gurobi.GRB.INTEGER)
x3 = model.addVar(name="milligrams_of_zinc", lb=0)

# Define the objective function
model.setObjective(8.92 * x1 + 6.55 * x2 + 4.0 * x3, gurobi.GRB.MINIMIZE)

# Add constraints
# Individual index constraints
model.addConstr(15 * x1 <= 93, name="immune_support_index_potassium")
model.addConstr(7 * x1 <= 146, name="digestive_support_index_potassium")
model.addConstr(7 * x1 <= 146, name="muscle_growth_index_potassium")
model.addConstr(8 * x1 <= 253, name="energy_stability_index_potassium")
model.addConstr(x1 <= 110, name="cardiovascular_support_index_potassium")

model.addConstr(2 * x2 <= 93, name="immune_support_index_vitamin_B3")
model.addConstr(5 * x2 <= 146, name="digestive_support_index_vitamin_B3")
model.addConstr(9 * x2 <= 146, name="muscle_growth_index_vitamin_B3")
model.addConstr(10 * x2 <= 253, name="energy_stability_index_vitamin_B3")
model.addConstr(12 * x2 <= 110, name="cardiovascular_support_index_vitamin_B3")

model.addConstr(13 * x3 <= 93, name="immune_support_index_zinc")
model.addConstr(10 * x3 <= 146, name="digestive_support_index_zinc")
model.addConstr(16 * x3 <= 146, name="muscle_growth_index_zinc")
model.addConstr(17 * x3 <= 253, name="energy_stability_index_zinc")
model.addConstr(10 * x3 <= 110, name="cardiovascular_support_index_zinc")

# Combined index constraints
model.addConstr(15 * x1 + 13 * x3 >= 10, name="combined_immune_support_index_potassium_zinc")
model.addConstr(2 * x2 + 13 * x3 >= 27, name="combined_immune_support_index_vitamin_B3_zinc")
model.addConstr(15 * x1 + 2 * x2 + 13 * x3 >= 19, name="combined_immune_support_index_all")

model.addConstr(5 * x2 + 10 * x3 >= 43, name="combined_digestive_support_index_vitamin_B3_zinc")
model.addConstr(7 * x1 + 10 * x3 >= 24, name="combined_digestive_support_index_potassium_zinc")
model.addConstr(7 * x1 + 5 * x2 >= 17, name="combined_digestive_support_index_potassium_vitamin_B3")
model.addConstr(7 * x1 + 5 * x2 + 10 * x3 >= 17, name="combined_digestive_support_index_all")

model.addConstr(7 * x1 + 16 * x3 >= 42, name="combined_muscle_growth_index_potassium_zinc")
model.addConstr(7 * x1 + 9 * x2 >= 30, name="combined_muscle_growth_index_potassium_vitamin_B3")
model.addConstr(9 * x2 + 16 * x3 >= 25, name="combined_muscle_growth_index_vitamin_B3_zinc")
model.addConstr(7 * x1 + 9 * x2 + 16 * x3 >= 36, name="combined_muscle_growth_index_all")

model.addConstr(10 * x2 + 17 * x3 >= 64, name="combined_energy_stability_index_vitamin_B3_zinc")
model.addConstr(8 * x1 + 10 * x2 >= 70, name="combined_energy_stability_index_potassium_vitamin_B3")
model.addConstr(8 * x1 + 17 * x3 >= 56, name="combined_energy_stability_index_potassium_zinc")
model.addConstr(8 * x1 + 10 * x2 + 17 * x3 >= 56, name="combined_energy_stability_index_all")

model.addConstr(x1 + 10 * x3 >= 36, name="combined_cardiovascular_support_index_potassium_zinc")
model.addConstr(x1 + 12 * x2 >= 29, name="combined_cardiovascular_support_index_potassium_vitamin_B3")
model.addConstr(x1 + 12 * x2 + 10 * x3 >= 29, name="combined_cardiovascular_support_index_all")

model.addConstr(4 * x1 - 7 * x2 >= 0, name="constraint_potassium_vitamin_B3")

model.addConstr(15 * x1 + 13 * x3 <= 58, name="upper_bound_combined_immune_support_index_potassium_zinc")
model.addConstr(15 * x1 + 2 * x2 <= 88, name="upper_bound_combined_immune_support_index_potassium_vitamin_B3")

model.addConstr(8 * x1 + 17 * x3 <= 147, name="upper_bound_combined_energy_stability_index_potassium_zinc")
model.addConstr(8 * x1 + 10 * x2 <= 213, name="upper_bound_combined_energy_stability_index_potassium_vitamin_B3")

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objval)
    print("Milligrams of potassium: ", x1.varValue)
    print("Milligrams of vitamin B3: ", x2.varValue)
    print("Milligrams of zinc: ", x3.varValue)
else:
    print("The model is infeasible")
```

## 6: Symbolic representation
The symbolic representation of the problem is as follows:

```json
{
    'sym_variables': [
        ('x1', 'milligrams of potassium'), 
        ('x2', 'milligrams of vitamin B3'), 
        ('x3', 'milligrams of zinc')
    ], 
    'objective_function': '8.92x1 + 6.55x2 + 4.0x3', 
    'constraints': [
        '15x1 <= 93', 
        '7x1 <= 146', 
        '7x1 <= 146', 
        '8x1 <= 253', 
        'x1 <= 110', 
        '2x2 <= 93', 
        '5x2 <= 146', 
        '9x2 <= 146', 
        '10x2 <= 253', 
        '12x2 <= 110', 
        '13x3 <= 93', 
        '10x3 <= 146', 
        '16x3 <= 146', 
        '17x3 <= 253', 
        '10x3 <= 110', 
        '15x1 + 13x3 >= 10', 
        '2x2 + 13x3 >= 27', 
        '15x1 + 2x2 + 13x3 >= 19', 
        '15x1 + 2x2 + 13x3 >= 19', 
        '5x2 + 10x3 >= 43', 
        '7x1 + 10x3 >= 24', 
        '7x1 + 5x2 >= 17', 
        '7x1 + 5x2 + 10x3 >= 17', 
        '7x1 + 16x3 >= 42', 
        '7x1 + 9x2 >= 30', 
        '9x2 + 16x3 >= 25', 
        '7x1 + 9x2 + 16x3 >= 36', 
        '7x1 + 9x2 + 16x3 >= 36', 
        '10x2 + 17x3 >= 64', 
        '8x1 + 10x2 >= 70', 
        '8x1 + 17x3 >= 56', 
        '8x1 + 10x2 + 17x3 >= 56', 
        'x1 + 10x3 >= 36', 
        'x1 + 12x2 >= 29', 
        'x1 + 12x2 + 10x3 >= 29', 
        '4x1 - 7x2 >= 0', 
        '15x1 + 13x3 <= 58', 
        '15x1 + 2x2 <= 88', 
        '8x1 + 17x3 <= 147', 
        '8x1 + 10x2 <= 213'
    ]
}
```