## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fiber', 'milligrams of vitamin B9', and 'grams of protein', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables and their corresponding natural language objects are:
- $x_1$ : 'grams of fiber'
- $x_2$ : 'milligrams of vitamin B9'
- $x_3$ : 'grams of protein'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $9.54x_1 + 4.75x_2 + 3.24x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
- $0.49x_2 + 1.04x_1 \geq 117$
- $1.04x_1 + 0.26x_3 \geq 109$
- $1.04x_1 + 0.49x_2 + 0.26x_3 \geq 109$
- $2.48x_1 + 0.8x_3 \geq 93$
- $2.48x_1 + 0.03x_2 \geq 46$
- $2.48x_1 + 0.03x_2 + 0.8x_3 \geq 46$
- $0.44x_1 + 2.45x_3 \geq 85$
- $0.44x_1 + 0.64x_2 \geq 34$
- $0.44x_1 + 0.64x_2 + 2.45x_3 \geq 34$
- $6x_2 - 8x_3 \geq 0$
- $2x_1 - 3x_2 \geq 0$
- $0.49x_2 + 0.26x_3 \leq 148$
- $0.44x_1 + 0.64x_2 \leq 156$

## 5: Consider the bounds and integrality constraints
- $x_1$ is continuous (no integer constraint)
- $x_2$ is continuous (non-fractional, implying integer but for Gurobi, this means using GRB_INTEGER)
- $x_3$ is continuous (non-fractional, implying integer but for Gurobi, this means using GRB_INTEGER)

However, given that $x_2$ and $x_3$ must be non-fractional (implying integers), we should technically use `GRB_INTEGER` for them, but since the problem description also mentions that there does not have to be an integer number of grams of fiber and you must have a non-fractional amount of milligrams of vitamin B9 and grams of protein, it seems there might be a confusion. For practical purposes and following the problem's likely intent regarding continuity:

## 6: Formulate the problem in Gurobi
Given the above, here is how you could formulate and solve the problem using Gurobi:

```python
import gurobi as grb

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

# Define the variables
x1 = m.addVar(name="grams_of_fiber", lb=0, ub=grb.GRB.INFINITY)
x2 = m.addVar(name="milligrams_of_vitamin_B9", lb=0, ub=grb.GRB.INFINITY, vtype=grb.GRB.INTEGER)
x3 = m.addVar(name="grams_of_protein", lb=0, ub=grb.GRB.INFINITY, vtype=grb.GRB.INTEGER)

# Define the objective function
m.setObjective(9.54 * x1 + 4.75 * x2 + 3.24 * x3, grb.GRB.MINIMIZE)

# Add constraints
m.addConstr(0.49 * x2 + 1.04 * x1 >= 117)
m.addConstr(1.04 * x1 + 0.26 * x3 >= 109)
m.addConstr(1.04 * x1 + 0.49 * x2 + 0.26 * x3 >= 109)
m.addConstr(2.48 * x1 + 0.8 * x3 >= 93)
m.addConstr(2.48 * x1 + 0.03 * x2 >= 46)
m.addConstr(2.48 * x1 + 0.03 * x2 + 0.8 * x3 >= 46)
m.addConstr(0.44 * x1 + 2.45 * x3 >= 85)
m.addConstr(0.44 * x1 + 0.64 * x2 >= 34)
m.addConstr(0.44 * x1 + 0.64 * x2 + 2.45 * x3 >= 34)
m.addConstr(6 * x2 - 8 * x3 >= 0)
m.addConstr(2 * x1 - 3 * x2 >= 0)
m.addConstr(0.49 * x2 + 0.26 * x3 <= 148)
m.addConstr(0.44 * x1 + 0.64 * x2 <= 156)

# Update model
m.update()

# Solve the model
m.optimize()

# Print the solution
if m.status == grb.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Grams of fiber: ", x1.varValue)
    print("Milligrams of vitamin B9: ", x2.varValue)
    print("Grams of protein: ", x3.varValue)
else:
    print("The model is infeasible")
```

## 7: Symbolic Representation
Here is the symbolic representation of the problem:

```json
{
    "sym_variables": [
        ["x1", "grams of fiber"],
        ["x2", "milligrams of vitamin B9"],
        ["x3", "grams of protein"]
    ],
    "objective_function": "9.54x1 + 4.75x2 + 3.24x3",
    "constraints": [
        "0.49x2 + 1.04x1 >= 117",
        "1.04x1 + 0.26x3 >= 109",
        "1.04x1 + 0.49x2 + 0.26x3 >= 109",
        "2.48x1 + 0.8x3 >= 93",
        "2.48x1 + 0.03x2 >= 46",
        "2.48x1 + 0.03x2 + 0.8x3 >= 46",
        "0.44x1 + 2.45x3 >= 85",
        "0.44x1 + 0.64x2 >= 34",
        "0.44x1 + 0.64x2 + 2.45x3 >= 34",
        "6x2 - 8x3 >= 0",
        "2x1 - 3x2 >= 0",
        "0.49x2 + 0.26x3 <= 148",
        "0.44x1 + 0.64x2 <= 156"
    ]
}
```