## Step 1: Define the symbolic representation of the problem
The symbolic representation of the problem consists of pairs of variables in symbolic notation and their corresponding natural language objects, the objective function rendered as an algebraic term, and the list of semi-algebraic constraints.

## Step 2: List the variables and their corresponding symbolic notation
Let's denote the variables as follows:
- blueberry pies: $x_0$
- apples: $x_1$
- chicken breasts: $x_2$
- green beans: $x_3$
- chicken thighs: $x_4$
- rotisserie chickens: $x_5$
- bagged salads: $x_6$

## Step 3: Formulate the objective function
The objective function to minimize is:
$4.69x_0x_1 + 4.68x_0x_5 + 7.01x_1x_3 + 9.9x_2x_3 + 3.66x_2x_5 + 1.95x_1 + 3.88x_6$

## 4: List the constraints
There are numerous constraints provided, including:
- Iron constraints: $8x_0 + 13x_1 + 12x_2 + 5x_3 + 5x_4 + 7x_5 + 9x_6 \leq 222$
- Calcium constraints: $10x_0 + 5x_1 + 7x_2 + 3x_3 + 4x_4 + 11x_5 + 5x_6 \leq 181$
- Sourness index constraints: $13x_0 + x_1 + 5x_2 + 3x_3 + 14x_4 + 10x_5 + x_6 \leq 195$
- Protein constraints: $14x_0 + 14x_1 + 14x_2 + 6x_3 + 5x_4 + 3x_5 + 4x_6 \leq 169$
- And many more constraints on iron, calcium, sourness index, and protein from various combinations of food items.

## 5: Provide the symbolic representation in JSON format
```json
{
'sym_variables': [
    ('x0', 'blueberry pies'),
    ('x1', 'apples'),
    ('x2', 'chicken breasts'),
    ('x3', 'green beans'),
    ('x4', 'chicken thighs'),
    ('x5', 'rotisserie chickens'),
    ('x6', 'bagged salads')
],
'objective_function': '4.69*x0*x1 + 4.68*x0*x5 + 7.01*x1*x3 + 9.9*x2*x3 + 3.66*x2*x5 + 1.95*x1 + 3.88*x6',
'constraints': [
    '8*x0 + 13*x1 + 12*x2 + 5*x3 + 5*x4 + 7*x5 + 9*x6 <= 222',
    '10*x0 + 5*x1 + 7*x2 + 3*x3 + 4*x4 + 11*x5 + 5*x6 <= 181',
    '13*x0 + x1 + 5*x2 + 3*x3 + 14*x4 + 10*x5 + x6 <= 195',
    '14*x0 + 14*x1 + 14*x2 + 6*x3 + 5*x4 + 3*x5 + 4*x6 <= 169',
    # Add all other constraints here...
    'x0 >= 0', 'x1 >= 0', 'x2 >= 0', 'x3 >= 0', 'x4 >= 0', 'x5 >= 0', 'x6 >= 0'
]
}
```

## 6: Output Gurobi code
```python
import gurobi

# Define the model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="blueberry_pies", lb=0, vtype=gurobi.GRB.CONTINUOUS)
x1 = model.addVar(name="apples", lb=0, vtype=gurobi.GRB.CONTINUOUS)
x2 = model.addVar(name="chicken_breasts", lb=0, vtype=gurobi.GRB.CONTINUOUS)
x3 = model.addVar(name="green_beans", lb=0, vtype=gurobi.GRB.CONTINUOUS)
x4 = model.addVar(name="chicken_thighs", lb=0, vtype=gurobi.GRB.CONTINUOUS)
x5 = model.addVar(name="rotisserie_chickens", lb=0, vtype=gurobi.GRB.CONTINUOUS)
x6 = model.addVar(name="bagged_salads", lb=0, vtype=gurobi.GRB.CONTINUOUS)

# Define the objective function
model.setObjective(4.69*x0*x1 + 4.68*x0*x5 + 7.01*x1*x3 + 9.9*x2*x3 + 3.66*x2*x5 + 1.95*x1 + 3.88*x6, gurobi.GRB.MINIMIZE)

# Add constraints
# Iron constraints
model.addConstr(8*x0 + 13*x1 + 12*x2 + 5*x3 + 5*x4 + 7*x5 + 9*x6 <= 222, name="iron_constraint")
# Calcium constraints
model.addConstr(10*x0 + 5*x1 + 7*x2 + 3*x3 + 4*x4 + 11*x5 + 5*x6 <= 181, name="calcium_constraint")
# Sourness index constraints
model.addConstr(13*x0 + x1 + 5*x2 + 3*x3 + 14*x4 + 10*x5 + x6 <= 195, name="sourness_index_constraint")
# Protein constraints
model.addConstr(14*x0 + 14*x1 + 14*x2 + 6*x3 + 5*x4 + 3*x5 + 4*x6 <= 169, name="protein_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("blueberry_pies: ", x0.varValue)
    print("apples: ", x1.varValue)
    print("chicken_breasts: ", x2.varValue)
    print("green_beans: ", x3.varValue)
    print("chicken_thighs: ", x4.varValue)
    print("rotisserie_chickens: ", x5.varValue)
    print("bagged_salads: ", x6.varValue)
else:
    print("The model is infeasible or unbounded.")
```