## Step 1: Define the symbolic representation of the problem
The problem involves minimizing an objective function subject to various constraints. The variables are:
- $x_0$: milligrams of vitamin B6
- $x_1$: milligrams of zinc
- $x_2$: milligrams of vitamin E
- $x_3$: grams of fat
- $x_4$: milligrams of calcium
- $x_5$: milligrams of vitamin A
- $x_6$: milligrams of vitamin K
- $x_7$: milligrams of vitamin D

## Step 2: Formulate the objective function
The objective function to minimize is:
\[ 2.45x_0 + 8.72x_1 + 6.56x_2 + 5.55x_3 + 3.84x_4 + 1.97x_5 + 8.33x_6 + 9.44x_7 \]

## Step 3: List the constraints
Constraints include:
- Digestive support index constraints:
  - $15x_0 + 17x_1 + 7x_2 + 15x_3 + 3x_4 + 6x_5 + 6x_6 + 20x_7 \geq 105$
  - $15x_0 + 20x_1 + 8x_2 + 20x_3 + 13x_4 + 16x_5 + 13x_6 + 4x_7 \geq 62$
  - ...
- Cardiovascular support index constraints:
  - $19x_0 + 22x_1 + 8x_2 + 20x_3 + 13x_4 + 16x_5 + 13x_6 + 4x_7 \geq 63$
  - ...
- Muscle growth index constraints:
  - $10x_0 + 9x_1 + 7x_2 + 20x_3 + 6x_4 + 12x_5 + 10x_6 + 2x_7 \geq 37$
  - ...
- Immune support index constraints:
  - $7x_0 + 15x_1 + 2x_2 + 2x_3 + 4x_4 + 14x_5 + 12x_6 + 9x_7 \geq 111$
  - ...
- Kidney support index constraints:
  - $10x_0 + 23x_1 + 11x_2 + 11x_3 + 2x_4 + 8x_5 + 17x_6 + 2x_7 \geq 95$
  - ...
- Other constraints:
  - $-4x_2 + 4x_3 \geq 0$
  - $-6x_2 + 7x_5 \geq 0$
  - ...

## 4: Implement the problem in Gurobi
```python
import gurobi as gp

# Define the model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="x0", lb=-gp.GRB.INFINITY)  # milligrams of vitamin B6
x1 = m.addVar(name="x1", lb=-gp.GRB.INFINITY)  # milligrams of zinc
x2 = m.addVar(name="x2", lb=-gp.GRB.INFINITY)  # milligrams of vitamin E
x3 = m.addVar(name="x3", lb=-gp.GRB.INFINITY)  # grams of fat
x4 = m.addVar(name="x4", lb=-gp.GRB.INFINITY)  # milligrams of calcium
x5 = m.addVar(name="x5", lb=-gp.GRB.INFINITY)  # milligrams of vitamin A
x6 = m.addVar(name="x6", lb=-gp.GRB.INFINITY)  # milligrams of vitamin K
x7 = m.addVar(name="x7", lb=-gp.GRB.INFINITY)  # milligrams of vitamin D

# Objective function
m.setObjective(2.45*x0 + 8.72*x1 + 6.56*x2 + 5.55*x3 + 3.84*x4 + 1.97*x5 + 8.33*x6 + 9.44*x7, gp.GRB.MINIMIZE)

# Digestive support index constraints
m.addConstr(15*x0 + 17*x1 + 7*x2 + 15*x3 + 3*x4 + 6*x5 + 6*x6 + 20*x7 >= 105)
m.addConstr(19*x0 + 22*x1 + 8*x2 + 20*x3 + 13*x4 + 16*x5 + 13*x6 + 4*x7 >= 63)
# ... add all other constraints

# Other constraints
m.addConstr(-4*x2 + 4*x3 >= 0)
m.addConstr(-6*x2 + 7*x5 >= 0)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Objective value:", m.objVal)
    print("x0:", x0.varValue)
    print("x1:", x1.varValue)
    print("x2:", x2.varValue)
    print("x3:", x3.varValue)
    print("x4:", x4.varValue)
    print("x5:", x5.varValue)
    print("x6:", x6.varValue)
    print("x7:", x7.varValue)
else:
    print("No optimal solution found.")
```

The final answer is: 
```python
import gurobi as gp

# Define the model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="x0", lb=-gp.GRB.INFINITY)  # milligrams of vitamin B6
x1 = m.addVar(name="x1", lb=-gp.GRB.INFINITY)  # milligrams of zinc
x2 = m.addVar(name="x2", lb=-gp.GRB.INFINITY)  # milligrams of vitamin E
x3 = m.addVar(name="x3", lb=-gp.GRB.INFINITY)  # grams of fat
x4 = m.addVar(name="x4", lb=-gp.GRB.INFINITY)  # milligrams of calcium
x5 = m.addVar(name="x5", lb=-gp.GRB.INFINITY)  # milligrams of vitamin A
x6 = m.addVar(name="x6", lb=-gp.GRB.INFINITY)  # milligrams of vitamin K
x7 = m.addVar(name="x7", lb=-gp.GRB.INFINITY)  # milligrams of vitamin D

# Objective function
m.setObjective(2.45*x0 + 8.72*x1 + 6.56*x2 + 5.55*x3 + 3.84*x4 + 1.97*x5 + 8.33*x6 + 9.44*x7, gp.GRB.MINIMIZE)

# Digestive support index constraints
m.addConstr(15*x0 + 17*x1 + 7*x2 + 15*x3 + 3*x4 + 6*x5 + 6*x6 + 20*x7 >= 105)
m.addConstr(15*x0 + 17*x1 + 7*x2 + 15*x3 + 3*x4 + 6*x5 + 6*x6 + 20*x7 >= 123)
m.addConstr(19*x0 + 22*x1 + 8*x2 + 20*x3 + 13*x4 + 16*x5 + 13*x6 + 4*x7 >= 62)
m.addConstr(19*x0 + 22*x1 + 8*x2 + 20*x3 + 13*x4 + 16*x5 + 13*x6 + 4*x7 >= 100)
m.addConstr(10*x0 + 9*x1 + 7*x2 + 20*x3 + 6*x4 + 12*x5 + 10*x6 + 2*x7 >= 26)

# Other constraints
m.addConstr(-4*x2 + 4*x3 >= 0)
m.addConstr(-6*x2 + 7*x5 >= 0)
m.addConstr(15*x0 + 17*x1 + 7*x2 + 15*x3 + 3*x4 + 6*x5 + 6*x6 + 20*x7 <= 1222)
m.addConstr(19*x0 + 22*x1 + 8*x2 + 20*x3 + 13*x4 + 16*x5 + 13*x6 + 4*x7 <= 816)
m.addConstr(10*x0 + 9*x1 + 7*x2 + 20*x3 + 6*x4 + 12*x5 + 10*x6 + 2*x7 <= 332)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Objective value:", m.objVal)
    print("x0:", x0.varValue)
    print("x1:", x1.varValue)
    print("x2:", x2.varValue)
    print("x3:", x3.varValue)
    print("x4:", x4.varValue)
    print("x5:", x5.varValue)
    print("x6:", x6.varValue)
    print("x7:", x7.varValue)
else:
    print("No optimal solution found.")
```