To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of pies made by the bakery.
- $x_2$ as the number of tarts made by the bakery.

The objective function is to maximize profit, where each pie yields a profit of $8 and each tart yields a profit of $5. Thus, the objective function can be represented algebraically as:

\[ 8x_1 + 5x_2 \]

The constraints are:
1. The total amount of blueberries used does not exceed 1000 units: \(5x_1 + 3x_2 \leq 1000\).
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: \(x_1 \geq 30\).

Symbolic representation of the problem:
```json
{
    'sym_variables': [('x1', 'number of pies'), ('x2', 'number of tarts')],
    'objective_function': '8*x1 + 5*x2',
    'constraints': ['5*x1 + 3*x2 <= 1000', 'x2 >= 3*x1', 'x1 >= 30']
}
```

Given this symbolic representation, we can now write the Gurobi code in Python to solve this linear programming optimization problem.

```python
from gurobipy import *

# Create a new model
m = Model("Bakery_Optimization")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="pies")
x2 = m.addVar(vtype=GRB.INTEGER, name="tarts")

# Set the objective function: maximize profit
m.setObjective(8*x1 + 5*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x1 + 3*x2 <= 1000, "blueberries_limit")
m.addConstr(x2 >= 3*x1, "tarts_to_pies_ratio")
m.addConstr(x1 >= 30, "minimum_pies")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution found: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```