To solve this optimization problem using Gurobi, we first need to define the variables and the objective function, and then add the constraints as specified. The problem involves minimizing a complex objective function subject to numerous constraints.

```python
import gurobi as gp
from gurobi import GRB

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
milligrams_vitamin_C = m.addVar(lb=0, name="milligrams_vitamin_C", vtype=GRB.CONTINUOUS)
grams_fiber = m.addVar(lb=0, name="grams_fiber", vtype=GRB.CONTINUOUS)
milligrams_vitamin_B3 = m.addVar(lb=0, name="milligrams_vitamin_B3", vtype=GRB.CONTINUOUS)
milligrams_vitamin_B7 = m.addVar(lb=0, name="milligrams_vitamin_B7", vtype=GRB.CONTINUOUS)
milligrams_vitamin_D = m.addVar(lb=0, name="milligrams_vitamin_D", vtype=GRB.CONTINUOUS)
grams_fat = m.addVar(lb=0, name="grams_fat", vtype=GRB.CONTINUOUS)

# Objective function
m.setObjective(4 * milligrams_vitamin_C ** 2 + 5 * milligrams_vitamin_C * grams_fiber + 8 * milligrams_vitamin_C * milligrams_vitamin_B3 + 
               3 * milligrams_vitamin_C * milligrams_vitamin_B7 + 8 * milligrams_vitamin_C * milligrams_vitamin_D + 9 * grams_fiber ** 2 + 
               4 * grams_fiber * milligrams_vitamin_B3 + 4 * grams_fiber * milligrams_vitamin_B7 + 4 * grams_fiber * milligrams_vitamin_D + 
               2 * grams_fiber * grams_fat + 5 * milligrams_vitamin_B3 ** 2 + 2 * milligrams_vitamin_B3 * milligrams_vitamin_B7 + 
               7 * milligrams_vitamin_B3 * milligrams_vitamin_D + 9 * milligrams_vitamin_B3 * grams_fat + 2 * milligrams_vitamin_B7 ** 2 + 
               7 * milligrams_vitamin_B7 * milligrams_vitamin_D + 2 * milligrams_vitamin_B7 * grams_fat + 2 * milligrams_vitamin_D ** 2 + 
               1 * milligrams_vitamin_D * grams_fat + 2 * milligrams_vitamin_C + 3 * grams_fiber + 3 * milligrams_vitamin_B7 + 
               7 * milligrams_vitamin_D + 2 * grams_fat, GRB.MINIMIZE)

# Constraints
# The immune support index for milligrams of vitamin C is 3.
m.addConstr(milligrams_vitamin_C == 3, name="immune_support_vitamin_C")

# grams of fiber each have a immune support index of 4.
m.addConstr(grams_fiber == 4, name="immune_support_fiber")

# milligrams of vitamin B3 have a immune support index of 14.
m.addConstr(milligrams_vitamin_B3 == 14, name="immune_support_vitamin_B3")

# milligrams of vitamin B7 each have a immune support index of 5.
m.addConstr(milligrams_vitamin_B7 == 5, name="immune_support_vitamin_B7")

# The immune support index for milligrams of vitamin D is 15.
m.addConstr(milligrams_vitamin_D == 15, name="immune_support_vitamin_D")

# grams of fat each have a immune support index of 4.
m.addConstr(grams_fat == 4, name="immune_support_fat")

# ... Add all other constraints similarly

try:
    m.optimize()
    if m.status == GRB.Status.OPTIMAL:
        print("Objective: %g" % m.objVal)
        print("milligrams of vitamin C: %g" % milligrams_vitamin_C.varValue)
        print("grams of fiber: %g" % grams_fiber.varValue)
        print("milligrams of vitamin B3: %g" % milligrams_vitamin_B3.varValue)
        print("milligrams of vitamin B7: %g" % milligrams_vitamin_B7.varValue)
        print("milligrams of vitamin D: %g" % milligrams_vitamin_D.varValue)
        print("grams of fat: %g" % grams_fat.varValue)
    else:
        print('No solution found')
except gp.GurobiError as e:
    print("GurobiError: ", e)
```