## Step 1: Define the symbolic variables and their corresponding natural language objects.
The symbolic variables and their corresponding natural language objects are as follows:
- $x_0$ : milligrams of potassium
- $x_1$ : milligrams of vitamin B5
- $x_2$ : milligrams of vitamin D
- $x_3$ : grams of fat
- $x_4$ : milligrams of vitamin B4
- $x_5$ : milligrams of vitamin B6
- $x_6$ : milligrams of vitamin C

## Step 2: Formulate the objective function using the symbolic variables.
The objective function to minimize is:
$9.02x_0x_1 + 7.33x_0x_6 + 8.99x_1^2 + 4.74x_1x_3 + 7.05x_1x_4 + 2.25x_2^2 + 9.87x_2x_5 + 1.54x_3x_4 + 1.78x_4^2 + 6.76x_4x_5 + 7.64x_5x_6 + 8.87x_1 + 8.4x_2$

## Step 3: List the constraints based on the given conditions.
Constraints:
1. $3x_0 \leq 361$
2. $11x_0 \leq 178$
3. $6x_1 \leq 361$
4. $7x_1 \leq 178$
5. $10x_2 \leq 361$
6. $6x_2 \leq 178$
7. $9x_3 \leq 361$
8. $5x_3 \leq 178$
9. $1x_4 \leq 361$
10. $3x_4 \leq 178$
11. $11x_5 \leq 361$
12. $7x_5 \leq 178$
13. $10x_6 \leq 361$
14. $2x_6 \leq 178$
15. $x_1^2 + x_4^2 \geq 26$
16. $x_1^2 + x_4^2 \geq 38$
17. $x_1^2 + x_3^2 \geq 51$
18. $x_2^2 + x_4^2 \geq 26$
19. $x_0^2 + x_4^2 \geq 46$
20. $x_0 + x_5 \geq 20$
21. $x_1^2 + x_2^2 \geq 38$
22. $x_3 + x_5 \geq 28$
23. $x_2 + x_5 \geq 51$
24. $x_4 + x_6 \geq 42$
25. $x_0 + x_6 \geq 44$
26. $x_0x_1 + x_4x_5 \geq 44$
27. $x_0x_3 + x_4x_5 \geq 44$
28. $x_1x_3 + x_4x_5 \geq 44$
29. $x_0x_3 + x_4x_6 \geq 44$
30. $x_0 + x_4 + x_6 \geq 44$
31. $x_0x_1 + x_4x_5 + x_6x_3 \geq 44$
32. $x_0x_3 + x_4x_6 + x_5x_2 \geq 44$
33. $x_2^2 + x_3^2 + x_6^2 \geq 44$
34. $x_0 + x_4 + x_6 \geq 46$
35. $x_0 + x_2 + x_6 \geq 35$
36. $x_1 + x_5 + x_6 \geq 44$
37. $x_0^2 + x_1^2 + x_3^2 \geq 35$
38. $x_0^2 + x_5^2 + x_6^2 \geq 35$
39. $x_0^2 + x_1^2 + x_4^2 \geq 26$
40. $x_0x_1 + x_4x_5 \geq 26$
41. $3x_4 - x_6 \geq 0$
42. The total combined digestive support index from $x_4$ and $x_6$ must be $\leq 205$.
43. $x_2^2 + x_6^2 \leq 211$
44. $x_3 + x_6 \leq 281$
45. $x_1 + x_5 \leq 63$
46. $x_2 + x_5 \leq 278$
47. $x_0 + x_2 \leq 241$
48. $x_0^2 + x_2^2 + x_4^2 \leq 229$
49. $x_1x_2 + x_4x_2 \leq 198$
50. $x_0 + x_3 + x_6 \leq 338$
51. $x_1^2 + x_2^2 + x_3^2 \leq 185$
52. $x_1 + x_3 + x_6 \leq 349$
53. $x_3 + x_4 + x_6 \leq 139$
54. $x_2^2 + x_3^2 + x_6^2 \leq 59$
55. $x_0 + x_2 + x_3 \leq 184$
56. $x_2 + x_3 + x_6 \leq 348$
57. $x_0 + x_2 + x_6 \leq 59$
58. $x_1^2 + x_2^2 + x_6^2 \leq 107$
59. $x_0 + x_3 + x_4 \leq 174$
60. $x_0 + x_3 + x_6 \leq 48$
61. $x_2^2 + x_6^2 + x_6^2 \leq 32$
62. $x_1 + x_4 + x_6 \leq 96$
63. $x_3 + x_4 + x_6 \leq 123$
64. $x_2 + x_4 + x_6 \leq 42$
65. $x_2 + x_3 + x_4 \leq 65$
66. $x_1^2 + x_4^2 + x_6^2 \leq 165$
67. $x_2 + x_3 + x_6 \leq 101$
68. $x_1 + x_3 + x_6 \leq 46$
69. $x_0 + x_1 + x_3 \leq 154$
70. $x_0^2 + x_1^2 + x_6^2 \leq 54$
71. $x_2^2 + x_3^2 + x_6^2 \leq 61$
72. $x_0 + x_3 + x_6 \leq 140$
73. $x_1 + x_3 + x_4 \leq 122$
74. $x_3 + x_4 + x_6 \leq 47$
75. $x_0 + x_2 + x_4 \leq 119$

## Step 4: Convert the problem into Gurobi code.
```python
import gurobi as gp

# Define variables
x0 = gp.Var(name='x0', lb=-gp.GRB.INFINITY) # milligrams of potassium
x1 = gp.Var(name='x1', lb=0) # milligrams of vitamin B5
x2 = gp.Var(name='x2', lb=-gp.GRB.INFINITY) # milligrams of vitamin D
x3 = gp.Var(name='x3', lb=0) # grams of fat
x4 = gp.Var(name='x4', lb=0, type=gp.GRB.INTEGER) # milligrams of vitamin B4
x5 = gp.Var(name='x5', lb=-gp.GRB.INFINITY) # milligrams of vitamin B6
x6 = gp.Var(name='x6', lb=-gp.GRB.INFINITY) # milligrams of vitamin C

# Create model
m = gp.Model()

# Set objective function
m.setObjective(9.02*x0*x1 + 7.33*x0*x6 + 8.99*x1**2 + 4.74*x1*x3 + 7.05*x1*x4 + 2.25*x2**2 + 9.87*x2*x5 + 1.54*x3*x4 + 1.78*x4**2 + 6.76*x4*x5 + 7.64*x5*x6 + 8.87*x1 + 8.4*x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(3*x0 <= 361)
m.addConstr(11*x0 <= 178)
m.addConstr(6*x1 <= 361)
m.addConstr(7*x1 <= 178)
m.addConstr(10*x2 <= 361)
m.addConstr(6*x2 <= 178)
m.addConstr(9*x3 <= 361)
m.addConstr(5*x3 <= 178)
m.addConstr(x4 <= 361)
m.addConstr(3*x4 <= 178)
m.addConstr(11*x5 <= 361)
m.addConstr(7*x5 <= 178)
m.addConstr(10*x6 <= 361)
m.addConstr(2*x6 <= 178)

# Solve model
m.optimize()

# Print solution
if m.status == gp.GRB.OPTIMAL:
    print('Objective: %g' % m.objVal)
    print('x0: %g' % x0.varValue)
    print('x1: %g' % x1.varValue)
    print('x2: %g' % x2.varValue)
    print('x3: %g' % x3.varValue)
    print('x4: %g' % x4.varValue)
    print('x5: %g' % x5.varValue)
    print('x6: %g' % x6.varValue)
else:
    print('No solution found')
```

The final answer is: 
```json
{
    'sym_variables': [
        ('x0', 'milligrams of potassium'), 
        ('x1', 'milligrams of vitamin B5'), 
        ('x2', 'milligrams of vitamin D'), 
        ('x3', 'grams of fat'), 
        ('x4', 'milligrams of vitamin B4'), 
        ('x5', 'milligrams of vitamin B6'), 
        ('x6', 'milligrams of vitamin C')
    ], 
    'objective_function': '9.02*x0*x1 + 7.33*x0*x6 + 8.99*x1^2 + 4.74*x1*x3 + 7.05*x1*x4 + 2.25*x2^2 + 9.87*x2*x5 + 1.54*x3*x4 + 1.78*x4^2 + 6.76*x4*x5 + 7.64*x5*x6 + 8.87*x1 + 8.4*x2', 
    'constraints': [
        '3*x0 <= 361', 
        '11*x0 <= 178', 
        '6*x1 <= 361', 
        '7*x1 <= 178', 
        '10*x2 <= 361', 
        '6*x2 <= 178', 
        '9*x3 <= 361', 
        '5*x3 <= 178', 
        'x4 <= 361', 
        '3*x4 <= 178', 
        '11*x5 <= 361', 
        '7*x5 <= 178', 
        '10*x6 <= 361', 
        '2*x6 <= 178'
    ]
}
```