## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of pies.
- $x_2$ represents the number of tarts.

## Step 2: Translate the objective function into symbolic notation
The objective is to maximize profit. Given that each pie yields a profit of $8 and each tart yields a profit of $5, the objective function can be written as:
\[ \text{Maximize:} \quad 8x_1 + 5x_2 \]

## 3: Translate the constraints into symbolic notation
The constraints given are:
1. The bakery only has 1000 units of blueberries: $5x_1 + 3x_2 \leq 1000$
2. The bakery must make at least three times as many tarts as pies: $x_2 \geq 3x_1$
3. The bakery must make at least 30 pies: $x_1 \geq 30$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

However, since $x_1$ and $x_2$ represent quantities of pies and tarts, they should naturally be non-negative, so we explicitly state:
- $x_1 \geq 30$ (from the problem statement)
- $x_2 \geq 0$ is implicitly required because $x_2 \geq 3x_1$ and $x_1 \geq 30$, ensuring $x_2$ is positive.

## 4: Correct and Complete Symbolic Representation
The complete symbolic representation is:
- Variables: $x_1$ (pies), $x_2$ (tarts)
- Objective Function: Maximize $8x_1 + 5x_2$
- Constraints:
  1. $5x_1 + 3x_2 \leq 1000$
  2. $x_2 \geq 3x_1$
  3. $x_1 \geq 30$

## 5: Convert to Gurobi Code
Now, let's convert this into Gurobi code in Python:

```python
import gurobi

def solve_bakery_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(lb=30, name="pies")  # At least 30 pies
    x2 = model.addVar(name="tarts")

    # Objective function: Maximize 8x1 + 5x2
    model.setObjective(8 * x1 + 5 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5 * x1 + 3 * x2 <= 1000, name="blueberry_limit")
    model.addConstr(x2 >= 3 * x1, name="tarts_to_pies_ratio")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: Pies = {x1.varValue}, Tarts = {x2.varValue}")
    else:
        print("No optimal solution found")

solve_bakery_problem()
```

However, I need to provide the output in the requested format:

```json
{
    'sym_variables': [('x1', 'pies'), ('x2', 'tarts')],
    'objective_function': '8x1 + 5x2',
    'constraints': [
        '5x1 + 3x2 <= 1000',
        'x2 >= 3x1',
        'x1 >= 30'
    ]
}
```

And the Gurobi code block remains as is but will be placed at the end as requested:

```python
import gurobi

def solve_bakery_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(lb=30, name="pies")  # At least 30 pies
    x2 = model.addVar(name="tarts")

    # Objective function: Maximize 8x1 + 5x2
    model.setObjective(8 * x1 + 5 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5 * x1 + 3 * x2 <= 1000, name="blueberry_limit")
    model.addConstr(x2 >= 3 * x1, name="tarts_to_pies_ratio")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: Pies = {x1.varValue}, Tarts = {x2.varValue}")
    else:
        print("No optimal solution found")

solve_bakery_problem()
```