## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin K' and 'milligrams of vitamin B6', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Define the attributes or resources for each variable
The attributes are given as:
- $r_0$: digestive support index
- $r_1$: energy stability index
- $r_2$: cognitive performance index
With specific values for $x_1$ and $x_2$:
- $r_0(x_1) = 9$, $r_0(x_2) = 8$
- $r_1(x_1) = 9$, $r_1(x_2) = 20$
- $r_2(x_1) = 11$, $r_2(x_2) = 17$

## 3: Express the objective function in symbolic notation
The objective function to minimize is $3x_1 + 6x_2$.

## 4: List all constraints in symbolic notation
1. $r_0(x_1) = 9$
2. $r_1(x_1) = 9$
3. $r_2(x_1) = 11$
4. $r_0(x_2) = 8$
5. $r_1(x_2) = 20$
6. $r_2(x_2) = 17$
7. $r_0(x_1) + r_0(x_2) \geq 28$
8. $r_1(x_1) + r_1(x_2) \geq 13$
9. $r_2(x_1) + r_2(x_2) \geq 29$
10. $-7x_1 + 9x_2 \geq 0$
11. $r_0(x_1) + r_0(x_2) \leq 57$
12. $r_1(x_1) + r_1(x_2) \leq 49$
13. $r_2(x_1) + r_2(x_2) \leq 59$

## 5: Substitute given attribute values into constraints
1. $9 \geq 9$ (always true)
2. $9 \geq 9$ (always true)
3. $11 \geq 11$ (always true)
4. $8 \geq 8$ (always true)
5. $20 \geq 20$ (always true)
6. $17 \geq 17$ (always true)
7. $9 + 8 \geq 28$ or $17 \geq 28$ (false)
8. $9 + 20 \geq 13$ or $29 \geq 13$ (always true)
9. $11 + 17 \geq 29$ or $28 \geq 29$ (false)
10. $-7x_1 + 9x_2 \geq 0$
11. $9 + 8 \leq 57$ or $17 \leq 57$ (always true)
12. $9 + 20 \leq 49$ or $29 \leq 49$ (always true)
13. $11 + 17 \leq 59$ or $28 \leq 59$ (always true)

## 6: Realize that some constraints are always true or false
Constraints 1-6 are always true. Constraints 7 and 9 are false, which makes the problem infeasible because they cannot be satisfied.

## 7: Conclusion on problem feasibility
Given that constraints 7 and 9 cannot be satisfied ($17 \geq 28$ and $28 \geq 29$ are both false), the problem as described is infeasible.

## 8: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin K'), ('x2', 'milligrams of vitamin B6')],
    'objective_function': '3*x1 + 6*x2',
    'constraints': [
        '9 >= 9',
        '9 >= 9',
        '11 >= 11',
        '8 >= 8',
        '20 >= 20',
        '17 >= 17',
        '9 + 8 >= 28',
        '9 + 20 >= 13',
        '11 + 17 >= 29',
        '-7*x1 + 9*x2 >= 0',
        '9 + 8 <= 57',
        '9 + 20 <= 49',
        '11 + 17 <= 59'
    ]
}
```

## 9: Gurobi Code
Despite the problem being infeasible, here's how you could set it up in Gurobi:
```python
import gurobi

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

# Define variables
x1 = m.addVar(name="milligrams of vitamin K", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = m.addVar(name="milligrams of vitamin B6", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Objective function
m.setObjective(3*x1 + 6*x2, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(9 >= 9, name="c1")
m.addConstr(9 >= 9, name="c2")
m.addConstr(11 >= 11, name="c3")
m.addConstr(8 >= 8, name="c4")
m.addConstr(20 >= 20, name="c5")
m.addConstr(17 >= 17, name="c6")
m.addConstr(9 + 8 >= 28, name="c7")
m.addConstr(9 + 20 >= 13, name="c8")
m.addConstr(11 + 17 >= 29, name="c9")
m.addConstr(-7*x1 + 9*x2 >= 0, name="c10")
m.addConstr(9 + 8 <= 57, name="c11")
m.addConstr(9 + 20 <= 49, name="c12")
m.addConstr(11 + 17 <= 59, name="c13")

# Optimize
m.optimize()

# Print status
print(m.status)

if m.status == gurobi.GRB.INFEASIBLE:
    print("The problem is infeasible")
```

The final answer is: 
```python
import gurobi

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

# Define variables
x1 = m.addVar(name="milligrams of vitamin K", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = m.addVar(name="milligrams of vitamin B6", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Objective function
m.setObjective(3*x1 + 6*x2, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(9 >= 9)
m.addConstr(9 >= 9)
m.addConstr(11 >= 11)
m.addConstr(8 >= 8)
m.addConstr(20 >= 20)
m.addConstr(17 >= 17)
m.addConstr(9 + 8 >= 28)
m.addConstr(9 + 20 >= 13)
m.addConstr(11 + 17 >= 29)
m.addConstr(-7*x1 + 9*x2 >= 0)
m.addConstr(9 + 8 <= 57)
m.addConstr(9 + 20 <= 49)
m.addConstr(11 + 17 <= 59)

# Optimize
m.optimize()

print(m.status)
if m.status == gurobi.GRB.INFEASIBLE:
    print("The problem is infeasible")
```