KeyError when converting Proto to DataFrame

# Problem
I am getting a KeyError when trying to convert the Meridian proto to a dataframe using the built-in functions. The model has a non-revenue KPI.

```
# Creating the proto data
summary_spec = marketing_processor.MediaSummarySpec(
include_non_paid_channels=False)
mmm_proto = mmm_ui_gen.create_mmm_ui_data_proto(
mmm=mmm,
specs=[
model_fit_processor.ModelFitSpec(),
marketing_processor.MarketingAnalysisSpec(
media_summary_spec=summary_spec,
),
],
time_breakdown_generators=[],
)

# Converting the proto to a dataframe
converter = dataframe_model_converter.DataFrameModelConverter(mmm_proto)
dataframes = converter()
```

# Environment
Meridian Version: 1.5.0

# Traceback

```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[7], line 16
14 print("Converting the proto to a dataframe")
15 converter = dataframe_model_converter.DataFrameModelConverter(mmm_proto)
---> 16 dataframes = converter()

File /opt/conda/lib/python3.10/site-packages/scenarioplanner/converters/dataframe/dataframe_model_converter.py:65, in DataFrameModelConverter.__call__(self, **kwargs)
63 for converter_class in self._converters:
64 converter_instance = converter_class(self.mmm) # pytype: disable=not-instantiable
---> 65 for table_name, table_data in converter_instance():
66 if output.get(table_name) is not None:
67 raise ValueError(f"Duplicate table name: {table_name}")

File /opt/conda/lib/python3.10/site-packages/scenarioplanner/converters/dataframe/marketing_analyses_converters.py:193, in _MarketingAnalysisConverter.__call__(self)
190 if not self._mmm.marketing_analyses:
191 return
--> 193 yield from self._handle_marketing_analyses(self._mmm.marketing_analyses)

File /opt/conda/lib/python3.10/site-packages/scenarioplanner/converters/dataframe/marketing_analyses_converters.py:235, in MediaOutcomeConverter._handle_marketing_analyses(self, analyses)
230 for marketing_analysis in analyses:
231 date_start, date_end = marketing_analysis.analysis_date_interval_str
233 baseline_outcome: mmm.Outcome = (
234 marketing_analysis.baseline_analysis.revenue_outcome
--> 235 if self._is_revenue_kpi
236 else marketing_analysis.baseline_analysis.non_revenue_outcome
237 )
238 # "contribution" == incremental outcome
239 baseline_contrib = baseline_outcome.contribution_pb.value.value

File /opt/conda/lib/python3.10/functools.py:981, in cached_property.__get__(self, instance, owner)
979 val = cache.get(self.attrname, _NOT_FOUND)
980 if val is _NOT_FOUND:
--> 981 val = self.func(instance)
982 try:
983 cache[self.attrname] = val

File /opt/conda/lib/python3.10/site-packages/scenarioplanner/converters/dataframe/marketing_analyses_converters.py:184, in _MarketingAnalysisConverter._is_revenue_kpi(self)
167 @functools.cached_property
168 def _is_revenue_kpi(self) -> bool:
169 """Returns true if analyses are using revenue KPI.
170
171 This is done heuristically: by looking at the (presumed existing) "baseline"
(...)
182 nonrevenue-type KPI defined, for example, only the former will be outputted.
183 """
--> 184 baseline_analysis = self._mmm.tagged_marketing_analyses[
185 dc.ANALYSIS_TAG_ALL
186 ].baseline_analysis
187 return baseline_analysis.maybe_revenue_outcome is not None

KeyError: 'ALL'
```